What is an AI-Powered Knowledge Base?

First of all, a clear understanding of what is a knowledge base is the key here. A knowledge base is a centralized repository for storing information that can be accessed by the users when searching for a piece of information.

We all know how AI is changing the world. AI in the knowledge base can change the way we organize the information, the way users can search the information, and make it SEO friendly. By now, you should be prompted on what an AI-powered Knowledge base means. Yes, an AI-powered Knowledge base uses advanced machine learning and Natural Language Processing technologies to understand user queries and make it a more easy-to-use platform.

At Document360, we have introduced Eddy, your new AI assistant with whom you can directly interact by asking questions and it will help you find more customized answers.  Eddy changes the way that your customers search and discover information and, therefore, how our knowledge base software operates too.

It’s not so much about if companies are going to adopt AI-powered knowledge bases as it is when. Since AI is transforming the nature of search for users, searching for content in the knowledge base is going to undergo the same changes. It already has. Content creation is also being revolutionized with generative AI, breaking the limits for authors who want to write content at scale.

Benefits of using an AI Knowledge Base for Customer Support

The AI-powered knowledge base completely transforms customer support by eliminating much of the manual work previously conducted by customer support teams and technical writers.

Reduce Response Time

Customers want instant answers to their queries, and with AI they can search the knowledge base with a few keywords and AI will return answers to them in a format completely suited to their needs. Instead of waiting around for agents to become free, customers can receive the answers they need immediately through AI. Businesses that can eliminate the need for customers to wait are more likely to make a sale or retain customers they already have.

Improved Search and Retrieval

Instead of the laborious process of typing and retyping search terms into the knowledge base, the old way of information retrieval is out. Ask AI anything, and you will receive tailored answers fully supported by the wealth of information in the knowledge base, without having to use the exact terms described in the article because AI picks up on search intent.

Personalized Content Recommendations

Customers using your knowledge base can receive personalized content recommendations that help with the learning experience. Suggestions about what to read next give customers deeper insight into your product and increase adoption as customers discover new features and use cases that would otherwise have remained unknown to them.

Automation of Repetitive Tasks

Exceedingly repetitive tasks can be automated with AI in your knowledge base, such as adding contextual tags to your articles, coming up with titles, or linking related articles together. AI automatically suggests tags and related articles to give a much richer information experience that helps your customers self-learn, freeing up technical writers to focus on the overall picture of the knowledge base and agents to provide support.

Enhanced User Experience

When customers have access to easy answers and technical writers can use AI to create content, the user experience of your knowledge base is much better for everyone. Article suggestions or even complete answers are automatically generated for customers, while your content producers don’t have to write everything from scratch.

Scalability

With AI powering your content creation efforts, it’s much more feasible to scale your customer support efforts without being restricted by budgets. Your agents and technical writers don’t have to write everything themselves and tags can be added at scale to improve efficiency. They can also use AI-powered analytics to understand content gaps and customer behavior in the knowledge base.

Data-Powered Insights

As we have just mentioned, AI uses data from your knowledge base to display key metrics and make insightful recommendations for improving your knowledge base. Health check metrics tell you at a glance how good your content is, from SEO metrics such as readability score and sentence length to readability metrics that make producing effective content much easier.

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How an AI Knowledge Base Helps the Customer Support Team?

Self-service doesn’t have to be a difficult experience; with AI’s help, your customer support team will perform better than ever.

Identifying Customer Queries in a Short Period

Ticket deflector enables you to identify customer queries that can be solved by the knowledge base far more quickly than an agent could respond to your customers. By creating common ticket deflectors and using AI to suggest relevant content, you can instantly solve many customer queries without lifting a finger.

Can Provide Accurate Answers with Reference Links

There is a high level of trust when it comes to using AI in the knowledge base since it will return answers to customers containing reference links that it has used to substantiate the reply. This gives a new level of transparency when using AI to answer customer queries, so customers can easily fact-check the information they receive since the facts are contained in an article.

Easily Create Content for New Queries

New queries that haven’t been solved by existing content can be addressed easily through AI. You can input the text for your new article and have AI change the tone or length automatically to reduce the time it takes for you to end up with a final product. AI writer allows you to use the power of generative AI to quickly create new content, combining human creativity with machine intelligence.

Gathering Insights into Customer Behavior

With an enhanced dashboard powered by AI, gathering insights into customer behavior on the knowledge base is easier than ever. Being able to view clicks and reading time down to the most granular detail enables you to use this knowledge to create better content by finding out what is working. By understanding exactly how customers interact with the knowledge base, you can analyze performance and identify customer needs.

Enhancing the Customer Support Experience

Customers are excited by the ability to access and find content in new ways since this makes the customer experience better than ever. Businesses that invest in the customer experience now are well-placed to beat the competition and ensure that superior customer support leads to better product adoption and loyalty. When businesses spend less time on creating content, they can invest both time and money back into the customer experience.

Gaining a Competitive Advantage with Automation

Automation gives you a competitive advantage by reducing the time it takes to perform certain actions. For example, you might utilize integrations with customer support software to automatically create knowledge base articles in your third-party app, seamlessly combining multiple tools. Eliminating unnecessary busy work saves all time and money that can be reinvested back into the business.

Creating a Centralized Repository for Relevant Content

Your content will be held centrally, allowing customers to discover relevant content as they search your knowledge base using AI. Customers won’t have to use the exact terms to find a piece of content since every article will be tagged with metadata and queries detected with NLP, adding a richer context to the article.

Utilizing Natural Language Processing for Common Questions

Natural Language Processing, or NLP, considers the relevant context surrounding the interaction. This means that NLP can respond in a much more nuanced and specific way for customers, sounding much more like a human than previous incarnations of AI.

Tips for Making AI Work for Your Knowledge Base

Now you know that implementing AI in your customer support is a good idea, here are a few tips for making that happen.

Create Lots of Helpful, Relevant Content

The key to making AI work in your knowledge base is filling it with helpful, relevant content. Although the AI is smart, it feeds off data that you create and then uses that data to construct answers for system users. The great thing is you create that content once, and it can be used infinitely to help as many customers as you like.

Constantly Update Content Based on Feedback

Some customers will tell you when your content isn’t hitting the mark, while others will churn without a word. Regularly solicit their feedback to understand when AI is meeting their needs and when you might need to create more content for a better customer support experience. Your first customers are like experiments who will alert you to the health of your content repository.

Use Analytics to Track Performance

An AI-powered knowledge base will come as in-built with advanced analytics reporting on content performance. For example, working out when customers are dropping off might show you that your content needs a bit of work in a particular area. Your most popular articles will require even more investment to ensure you are truly helping customers.

Deeply Understand Your Target Customers

Your customer base is unique, and any content you create for AI should specifically address their needs. Generic content will likely result in generic answers that fail to help customers in their most vulnerable moments and will have them turn to customer support in frustration. A well-targeted knowledge base will anticipate customers before they even have to ask, showing them you understand them well.

How Document360 Helps Your Business?

We have recently released Document360 2.0, which is a total upgrade of our software to incorporate the latest AI for our customers. Eddy is our AI assistant, who you can deploy to create content lightning fast or use to answer customer questions when your knowledge base is live. Document360 2.0 is redefining the knowledge base space and changing the way users interact with knowledge.

Document360 2.0 has every single feature we have talked about and more.

AI title recommender:
Document360 uses Natural Language Processing to identify critical information in the content and suggest suitable titles for the articles while preserving the original context.

AI article summarizer:
This feature is useful when readers want to save time by avoiding lengthy articles. Instead, they can get an idea from the condensed summary containing essential information from the article.

AI tag manager:
The feature analyzes the content, recognizes common keywords, and identifies topics to provide a set of suggested tags.

AI Search feature:
This New AI search feature uses AI algorithms to understand users’ prompts and queries and serve them with accurate information. This saves users time in searching for information in each article.

The only way to find out more is to give us a trial and see how Document360’s AI-powered knowledge base will supercharge your business.

 Switch to modern customer knowledge base now!

AI prompt templates 

An AI prompt template is a fully customizable prompt that helps technical writers produce an outcome. It contains a clear set of instructions and has a lot of context in detail. It also has configured input data and output formats. The benefits of AI prompt templates include 

• Time savings – Instead of constructing a new prompt every time, you can use these vetted and pre-defined templates to accomplish your goals quickly. This leads to productivity gains
• Reusability – AI templates can be reused multiple times by technical writers who span across multiple teams inside your organization
• Consistency – AI prompt templates provide consistency in how outputs are being generated 

7 AI prompt templates addressing various business use cases

Whether you’re building a Learning Management System, generating quick support ticket responses, seeking clarity on complex concepts, or generating quick, concise answers, these prompt templates save a lot of time and effort. This article addresses 7 AI prompt templates that cater to various business use cases.

1. AI prompt template to build a Learning Management System

If you are part of the technical writing team, there are chances that you will be collaborating with your Human Resources team to help build a Learning Management System for your internal and external stakeholders. You might be assigned a task to produce Multiple Choice Questions on various aspects of your software product or any services. Producing Multiple-choice Choice Questions is a laborious task that consumes a lot of time. Eddy can assist you in producing questions and answers on various topics based on your knowledge base content. Here is the AI prompt template 

  I am creating a learning management system for my organization. Create <number> multiple choice questions along with correct answers on the <topic> 

The placeholders for numbers and topics can be customized. Here are some examples from the Document360 knowledge base site.  

2. AI prompt templates to generate quick responses for support tickets

The customer support team are a natural ally to the technical writer’s team given the shared responsibilities to collaborate to increase customer satisfaction. If a customer support team receives a query on any “how-to” procedures or processes, Eddy can help customer support staff. Here is the AI prompt template 

Write an email to a customer giving a list of steps to <procedure topic> 

The placeholders for the procedure topic can be customized. Here is an example from the Document360 knowledge base site. 

3. AI prompt templates to seek clarity on business concepts

If your customers are seeking information clarity on some complex concepts in your software product features or any services you offer, then Eddy can help your customers with articulating complex concepts in simple terms. Here is the AI prompt template 

Explain the <concept>; the First paragraph should say what it is; the Second paragraph should say when to use it; the Third paragraph should summarise the first and second paragraph 

The placeholders for the concept can be customized. Here is an example from the Document360 knowledge base site. 

4. AI prompt templates to get quick and short answers

If your customers want short answers to some of the questions, Eddy can do that! Here are some examples of “How” and “What” questions where Eddy provided an exact answer!  

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5. AI prompt templates for comparisons

If your customers compare a set of features with others, there is no need to create a comparison for each topic. If content is present in your knowledge base, Eddy can produce comparison tables! Here is the AI prompt template 

Compare <feature 1> and <feature 2> with <product name> in table format 

The placeholders for feature and product name can be customized. Here is an example from the Document360 knowledge base site.  

6. AI prompt templates to seek answers to “why” questions

Another typical use case is that your customer might want to know the purpose of a software product feature or any policies of your organization, and then Eddy can assist your customers! Here is the prompt template 

Why do I need <feature name > / <policy name> 

The placeholders for the feature name and policy name can be customized. Here is an example from the Document360 knowledge base site.  

7. AI prompt templates for language translation on the fly

Even if you do not have a multilingual knowledge base, Eddy can translate content on the fly for your customer’s questions. Here is an example,

Data Privacy and Data Security 

Ask Eddy is built with state-of-the-art encryption and security protocols. The underlying technology uses AES 256-bit encryption for data at rest and uses TLS1.2 for HTTPS protocols for data in transit. Eddy is security-hardened against prompt injection attacks. To ensure data privacy, none of the questions and responses are stored for training any Large Language Models of the third-party providers we use. 

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Closing remarks 

Prompt engineering is emerging as another skill set requirement for technical writers. If you are utilizing “Ask Eddy” in your knowledge base, these AI prompt templates shall help you maximize the usage of Eddy for various use cases. These AI prompt templates can also be used in various other ChatGPT-like systems such that these prompt templates can be modified. These prompt templates address various use cases for both internal and external stakeholders.  

Characteristics of GenAI-based agents

If your customers are utilizing ChatGPT-like assistive search, and your existing content is not tailored to accommodate the characteristics of GenAI-based agents, it is high time to conduct a content audit. The underlying content must be GenAI-friendly to ensure that it serves your customers with trustworthy responses. The GenAI-based agents are text-hungry thus underlying content must be as explanatory as possible. More importantly, the underlying content must be written in a conversational style in a more generic persona. Guidelines to write content for GenAI-based agents are evolving. The technical writer’s community is also suggesting various tips for improvising the existing content. Let’s look at some of the emerging guidelines to produce GenAI-friendly content.

Also Checkout: How Technical Writers Can Utilize ChatGPT?

Top 8 Guidelines to Create GenAI-friendly Content

Writing content that is easily understood by GenAI-based agents involves incorporating clear language, structured formatting, and adherence to some specific guidelines. These guidelines are listed below:

Guideline #1: Write elaborate content

Rather than choosing brevity for your content, write the content as explanatory as possible. Elaborate content with more information that helps GenAI-based agents get a holistic perspective of the topic covered in your article. Use simple English words to write content rather than bombastic words. This helps in assimilating content and helps to answer your user queries. E.g. For this getting started article from Airtable is about a 16-minute read.

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What is GenAI?

It’s highly likely you will have heard of GenAI, such as ChatGPT, but other examples include Dall-E and Google Bard. To understand how GenAI works, you need to know about the concept of a prompt – inputting a command into the GenAI interface, such as text, which the system then processes to produce an output.

ChatGPT is a Large Language Model (LLM) developed by OpenAI with a highly simple user interface that anyone can use to submit prompts (instead of using an API, which was required by previous iterations of this type of technology). You could ask ChatGPT to “Tell me how to debug the code to solve the given error.”

AI algorithms then return solutions to the user, based on analyzing and indexing large datasets to provide a convincingly human response. This means that technical writers could feasibly have the bare bones of a documentation project and use a GenAI tool such as ChatGPT to write the content for them.

ChatGPT currently has more than 100 million users, with 1.6 billion visits in June 2023.

The Evolution of Technical Writing

The development of GenAI technology in recent years has augmented a shift in the landscape of technical writing. More sophisticated tools such as ChatGPT allow technical writers to generate creative content automatically, which is a departure from previous AI systems.

Technical writers are valued for their domain-specific knowledge and their highly developed writing capabilities, so they are the perfect overseers of this particular kind of Artificial Intelligence.It takes a trained professional to use a tool like ChatGPT to write technical content successfully; otherwise, there would be no way of verifying the accuracy of the output.

Technical writers who are comfortable with the Large Language Models of GenAI will become professionals with AI capabilities. This is likely to create further respect for the role of the technical writer in creating products because they will become masters of the technologies as well as proficient in communication.

Also, Check out our article on ChatGPT for technical writing

How GenAI Enhances the Technical Writer’s Capabilities

Consider using GenAI to enhance your technical writing capabilities in several ways.

Generating Ideas and Prompts

Technical writers embarking on a new project may wish to inspire the creative process through using GenAI to generate ideas and prompts. For example, you could ask ChatGPT to list 10 documentation benefits, which it will then do much faster than any human could. The same could be done for technical writing – ask the GenAI to create an outline for a new documentation article.

Improved Grammar and Syntax

GenAI can help you with the grammar and syntax for your technical writing. Since GenAI has been trained on huge datasets, it has achieved a human-like proficiency with language, although admittedly still somewhat robotic. Technical writers may already be used to tools like Grammarly and Hemingway helping with their writing, and GenAI can perform the same function.

Spelling and Proofreading

Similarly, GenAI can operate as a highly effective spell checker and proofreader, which is important for technical writers creating high-quality content. Errors reduce the trust in the quality of your content and may even render it incomprehensible, so using GenAI in this way improves quality. Since GenAI automatically checks the content, it can catch more mistakes than human proofreaders could.

Content Optimization

Optimizing your content for both search and usability is possible using a tool such as ChatGPT. If you’re trying to optimize your content for SEO, ChatGPT can provide you with related keywords, metadata, and descriptions. Optimizing content for users could involve helping you to write in a more concise way and eliminate unnecessary words and phrases. In this way, GenAI makes your content, more accessible which is essential when it comes to user-focused help content.

Generating Code and Technical Documentation

ChatGPT is well-known for being able to generate code and can even produce the basics of technical documentation for you. Since the GenAI is not sentient, the input of a human user will always be required to oversee the output of the tool, but it can certainly speed up the process of needing to write code samples and document them. You can even use GenAI to check and debug your code for errors, saving you valuable time when producing the documentation.

Personalized Content

You can use GenAI to create more personalized content for users by curating the tone, style and sentiment of the text to appeal to specific groups of users. GenAI can sense the peculiarities of language to some extent and generate text that matches the requirements of a particular audience, so you could submit your own documentation as a prompt and ask GenAI to tailor it for you. Many users find the text produced by tools such as ChatGPT eerily human-like, adding to its popularity.

Cross-Cultural Communication

You can use a tool such as ChatGPT to facilitate cross-cultural communication by utilizing its native translation capabilities, which you can use to translate text in real time. This means your technical content can easily be aimed at multiple audiences speaking different languages without the need to hire a professional translator. Multilingual documentation helps make your product more accessible globally.

Collaborative Writing

When generating the prompts for GenAI tools, you can gather the input of other stakeholders and departments to create content that is more collaborative. As long as you are cognitive of the specific requirements, content generated by GenAI meets multiple goals and can be iterated over time.

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Key advantages of GenAI

Here are the main advantages of using GenAI to improve your technical writing.

Improved Content Generation

First and foremost, content generation will be better with GenAI because you are using the power of Artificial Intelligence to analyze large datasets to inform your text. This is far wider research than any human technical writer would be capable of in a reasonable timeframe, as long as the technical writer has the skills and expertise to sense-check the final document.

Faster Document Production

You will be able to produce your documentation much faster using GenAI, since available tools can help you create a sample text with only a few simple prompts. Once the technical writer has learned how to effectively use GenAI, such as ChatGPT, building an initial document is a much faster process than assembling your text by hand. Using GenAI to proofread and optimize saves a lot of time because these processes can be automated.

Time and Cost Savings

Replacing manual labor with technology inevitably has significant time and cost-savings, once you have recouped the initial investment. GenAI can perform the work of many technical writers in terms of the scope of its abilities in researching and presenting content. Learning a tool like ChatGPT is also relatively simple due to its intuitive user interface, which is an improvement on previous iterations using APIs for access.

Enhanced Consistency

You can use GenAI to format for consistency in your documentation without manually changing your content. You can ask a tool such as ChatGPT to standardize your text and ensure you speak with one voice. GenAI will be able to review your documents much more rigorously than you could yourself, or enlisting the help of a third party.

Multilingual Capabilities

As we’ve already mentioned, GenAI can come in-built with multilingual capabilities, helping your content to reach a wider audience. It’s still advisable to employ a professional translator to review the content, but GenAI does much of the heavy lifting. GenAI works best when translating from English to other languages.

24/7 Availability

Unlike human workers, GenAI is always available for your needs. It never gets tired, and is capable of creating large amounts of text almost instantly. For technical writers who can use GenAI effectively, it’s like having a highly productive assistant to support your content creation efforts, and even achieve feats that are out of reach for humans.

Also, Check out: Must Attend Technical Writing Conferences of 2024

Limitations of GenAI

Although GenAI has huge potential, we’re now going to consider some of the limitations of using this type of new technology.

Quality and Coherence

As with all automatically generated content, technical writers are burdened with checking the output for quality and coherence. There is no guarantee that the documentation you produce with GenAI actually makes sense or is factually accurate, since the machine is totally reliant on the data that it has been given. It is best used as a starting point which expert technical writers can then use to mold to their own ends.

Bias and Fairness

GenAI models can absorb the bias and unfairness of the data they are trained on, which can then be reflected in your content. GenAI has no ethical judgment, so it can’t tell whether content is racist, sexist, and so on. In 2016, Microsoft had to halt its chatbot Tay after it began posting abusive messages on Twitter, and GenAI is also prone to hallucinations.

Control and Fine-Tuning

In order to produce the right documentation, GenAI needs to have been trained on the right datasets which are not necessarily up-to-date or accurate. Control and fine-tuning is required to ensure that the system generates the proper output, and is not necessarily achievable on the first try – or even the second.

Lack of Creativity

Although the results of GenAI are very impressive, it lacks the capability for true creativity. It’s reliant on your technical writers to input the prompts in order to generate the right text, and if the prompts are wrong then your content won’t hit the mark. GenAI also has the tendency to sound slightly robotic since it can’t achieve the nuances of language that define the content of human writers.

Resource Intensiveness

At the moment, GenAI requires large amounts of computational power to generate images and this could be limiting for technical writing purposes. Thorough and accurate documentation is necessarily comprehensive and this places a burden on your resources that can be hard to overcome.

Data Privacy and Security

Data privacy and security are big concerns when it comes to any technology but especially GenAI. It’s critical to ensure you have permission to use the data involved in GenAI processes and that the data is protected, and that you comply with regulatory requirements.

Legal and Ethical Issues

Whether or not GenAI should be used in creating content is a matter for the law and ethics. GenAI tools do not attribute the source of their content, and users may also be expecting documentation that humans have written.Since content should ideally be sourced and attributed to its original authors, GenAI presents problems in this context.

Human Verification and Oversight

It’s not possible to create automated content with GenAI and immediately release it as a final output. Each textual document that you create must be closely vetted and overseen by technical writers who have domain-specific expertise. You cannot simply outsource the task of creating content to machines without the contribution of human experts.

What is Software Architecture Documentation?

Software architecture documentation is the thorough documentation of a software system’s architecture, including deliberate design decisions, components, and some specific artifacts such as diagrams, specs, and descriptions. It tells you how and why the code was built the way it was and enables team members and clients to understand and improve the software.

• Software architecture documentation aims to document these areas of the code:
• The non-functional requirements of the system
• The goals of the system

The decisions driving the architecture and the reasoning behind them

So, while good code will naturally speak for itself, there are some aspects of the software architecture that are not self-explanatory, and this is where good documentation comes in. It makes future maintenance and updating of the software a much more feasible process.

Software architecture documentation is usually aimed at these interested parties:

• Developers
• Software architects
• Testers
• QA
• Support
• Clients
• Ops
• Project managers

And anyone else who has a stake in how the software solution has been architected. If you don’t document the software architecture, you run the risk of losing track of why and how it has been built, potentially reversing and damaging previous choices when you make changes.

Why Should We Document Software Architecture?

We’ve just touched on why software development teams might want to document their software architecture, and we’re now going to look at the reasons for doing so in more depth.

Knowledge Sharing

While documentation is often low on the task list of many technical contributors, it’s essential for knowledge sharing in the domain of software architecture. Team members may forget why decisions were made over time and risk changing the software in a way that is not in line with the original mission. Documenting software architecture means that development teams can better share knowledge and preserve that knowledge for future contributors, who may be entirely different to the original creators.

Collaboration

Proper software architecture documentation enables teams to collaborate more effectively because stakeholders from across the board can understand the system. Intentions behind the code that are not immediately obvious gain more clarity and even less technical users can understand how and why the code functions the way it does, enabling better and more practical business decisions.

Scalability

In order to scale a project, you need to document the design decisions behind the architecture, specifications, and other technical details. Your team and architecture cannot grow if not properly documented, as vital information will be lost, and your software may become destined to fail. When first embarking on your software, your scope may be limited, but this will likely change over time as you embrace more features and use cases.

Reduce Maintenance Cost

If your software is to develop and keep pace with customer demand, your developers will need to perform regular maintenance of the code to ensure bugs are fixed and so on. If your software architecture is properly documented, this means any developer – even new ones – can theoretically jump in and confidently make changes. This reduces the maintenance cost of the code as updates and patches are easier.

Maintaining and Modernizing Outdated Software

As your software evolves, it must also meet different and increasingly stringent requirements, but stakeholders can often lose track of the software due to the pace of change. Software must be maintained and more importantly, modernized and that requires updated software architecture. Robust documentation tells you what changes need to be made and where you might be failing to meet standards.

Decision Support

The right documentation supports decision-making as architects, developers, project managers, and other parties responsible for driving the have access to more information. Although some like to think the simply looking at the code provides all the necessary insight, the reality is that intentions and context are completely lacking from this approach. Software architecture documentation fills the gap.

Also read: What is Software Documentation? Its types and Best practices

How to Create Software Architecture Documentation

Now, we’re going to go through the steps you need to take when creating your software architecture documentation.

Understand the Audience and Purpose

As with all forms of writing, you need to understand your software architecture documentation’s intended audience or audiences. You might initially think of other software architects, but audiences could also include developers, technical writers, project managers, and clients. It’s usually sensible to have different documents that are aimed at discrete audiences since the information that might be relevant to some could be distracting or overwhelming for others.

Gather Existing Information

It’s likely that the documentation you want to create for your software architecture may already exist in some form. If you gather existing materials, this will save you time in the documentation process and ensure you are making the best use of your resources. Taking this approach makes it more likely that all your collateral is up-to-date and accurate, and keeps all your important information in one place.

Choose a Documentation Format

You’ll need to decide whether you want to present your documentation as images, text, video, or some combination of the above. Different formats will require varying resources and be harder to update or translate into multilingual content as time goes on. Take into account which format best suits your users and has the lowest maintenance cost to ensure ongoing commitment to the documentation.

Outline the Documentation

Before you set to work creating large amounts of software architecture documentation, make sure you build an outline first, so you understand how it all fits together. It’s likely that you will have many collaborators involved in your documentation efforts so everyone needs to have a roadmap they can work with, just like they would with the software code.

Change Management and Versioning

Your software architecture documentation will change over time, so you’ll need to have a formal change management system in place as well as provisions for versioning. Versions should be updated with the original version kept intact in case there is ever a dispute or need for reversal, with everyone on the team kept informed of the latest version of your documentation.

Include Appendix and References

In the process of creating your software architecture documentation, you will likely make references to external sources and materials. Make sure you include an appendix and references so documentation users can look up the original sources and find out more information, ensuring that your documentation is comprehensive and reliable.

Regularly Maintain and Update

The final product of your software architecture documentation is never finished and must be adapted as the system is improved, changed, and updated. High-quality documentation accurately reflects the particulars of the system and gives users faith that it is actually useful. This requires regular maintenance and updating of the documentation as your software architecture evolves while preserving the original versions for reference.

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Best Practices for Software Architecture Documentation

Now consider these best practices for implementing software architecture documentation.

1. Implement Documentation in the Development Phase

Thorough documentation should be considered part of your prototype rather than an afterthought if you have time. Documentation should be as important as the code because it provides insights and information to key stakeholders who are developing the software. Important documents should be produced alongside the code to keep pace with an evolving product.

2. Document Only What You Need and Keep it Up-to-date

Thorough documentation does not mean you document everything – you should document only what you need or risk overwhelming and alienating your users who find the documentation too imposing. Documentation that is concise, relevant, and up-to-date will better serve the needs of your users than extensive, long-winded documents. Just enough documentation, and not too much, is the goal to aim for here.

3. Document for Different Stakeholders

As we’ve already discussed, you’ll need different forms of documentation for different stakeholders. There are a number of roles within the software development team who might be interested in software architecture documentation, who are the following:

Developers

Of course, developers will be interested in the particulars of the software system including specifications, dependencies and component relationships. In order to develop code most effectively, developers must understand the software architecture, thus enabling them to avoid breaking things or making sub-optimal changes.

Testers

Testers are responsible for intentionally trying to break the software to check for weak points, and therefore, they must have an intimate understanding of its architecture and design decisions so they can create effective test cases.

Project managers

Project managers must have a broad-level overview of the software architecture to help them understand what is possible and drive projects forward. Allocating resources requires appreciating the limits of the software and the skills required to complete certain milestones in a reasonable time.

Technical writers

Technical writers absolutely must know the system architecture in order to create effective and useful documentation for users. All documentation is interconnected and is needed to inform the authors of different types of docs. Software architects who are interested in documentation may also benefit from the help of professional technical writers.

4. Avoid Ambiguity and Be Concise

When your stakeholders are looking for documentation about the software architecture they need you to avoid ambiguity and be concise. If you make reference to a specific component then make sure to be consistent with your naming conventions and terminology.

5. Granular Accessibility

Granular accessibility is important for users searching for specific information within your documentation portal, so you’ll need to combine the capabilities to search for content with restricted access for some users and content. Keeping results relevant and useful is key for the adoption of your documentation.

Documentation Techniques in Software Architecture

Now we’ll look at these common techniques in documentation for software architecture.

Diagrams

Sometimes there is no better way to express your software architecture than through a visual diagram, typically using Unified Modeling Language (UML). If you want to explain your system’s design to users, including how the system parts function together, and how information flows between different parts of the system, then diagrams are a useful tool.

Textual Documentation

On the other hand, text is sometimes the only way to get a more complex point across, which is especially relevant when documenting your software architecture. Using textual documentation can help you explain high-level concepts, detail the functionality of components and more.

Hybrid Documentation

Of course, using a combination of diagrams and text can often be the best solution to presenting your documentation for a diverse user base. Diagrams can be as complex as you like with text accompanying them to explain what you mean.

Software Architecture Document Template

Here’s a common software architecture document template according to arc42. It’s open source and completely free to use, removing the pain of constructing your software architecture documents.

arc42 template

Image source

Document360 for Software Architecture Documentation

Document360 is an exceptional platform designed to streamline and elevate the process of creating and managing software architecture documentation. In the realm of software development, clear and comprehensive documentation is a critical component for successful project execution, collaboration, and knowledge retention. Document360 provides a user-friendly and powerful solution tailored specifically for software architects, developers, and technical writers, allowing them to create, maintain, and share software architecture documentation with ease and efficiency.

Also read: Best Process Documentation Software

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Wrapping Up

Ultimately, the people developing, updating, maintaining, and adding new features to the software may not be the ones who originally built it. For this reason, as well as others mentioned earlier, it’s therefore a very good idea to document your software architecture to ensure that your software continues to function effectively.
Without the proper documentation, software teams can descend into chaos and lose track of where they are going. Software architecture becomes impenetrable as engineers leave their positions and their replacements have no idea why certain decisions were made.

While documentation may not always be a priority for software architects, your team members and users will thank you for making the effort.

Understanding ChatGPT

Chat Generative Pre-Training Transfer, now popularly known as ChatGPT, is a language model developed by OpenAI. It needs prompts to generate content that answers a certain query from users. ChatGPT successfully produces content because it has been trained with a mammoth of data online.

From writing a sentence and then paragraphs to generating interesting poems and even marketing ads, ChatGPT does it all. It runs on the “unconditional generation” technique called a transformer using neural network architecture that. With the help of this transformer, ChatGPT naturally produces content, therefore giving hints of human touch.

Mechanics of ChatGPT

There’s more to the mechanics of ChatGPT other than the transformer architecture.

We asked ChatGPT about its mechanics, and here’s what it says:

With the right prompt, we understood ChatGPT can go back to the conversation history with the user and focus on pivotal data to generate natural-sounding responses. But other than that, here’s what it does:

• It breaks down the inputs from its users into smaller units called tokens.
• The tokens are then converted into contextual embeddings that help this AI tool to understand the context of the conversation.
• Once it understands the input context, ChatGPT uses the transformer technique to generate a fixed-length representation called a context vector.
• The transformer also includes an attention mechanism that focuses on different input parts and allows ChatGPT to generate meaningful answers.
• It also uses conditional generation that allows the AI tool to generate content for each token.

Besides these, ChatGPT also uses sampling strategy, conversational context, user interaction, and feedback loop to bring together a response that meets the user’s needs.

Challenges in Ensuring Accuracy and Trustworthiness

Even after having such a detailed process, certain challenges hamper the results generated by ChatGPT and make users question its accuracy and trustworthiness. We’ve listed the top challenges for you below.

1. Lack of human-like common sense

ChatGPT is smart and, mind you, witty as well. It answers the questions well, and if it doesn’t, it still offers variations to meet the user’s expectations.

But this smart tool lacks common sense.

For example, if you keep receiving irrelevant answers, it doesn’t share any new responses after a certain limit. It keeps sharing the same answers repeatedly. It goes on to show that it lacks common sense, and, therefore may end up wasting user’s time and efforts.

2. Requires Human interaction to generate appropriate responses

Another challenge that ChatGPT users often face is either repeating themselves or refining their questions until they get the right response. That means ChatGPT is wholly dependent on human prompts rather than narrowing down the base of the initial query to share a response with its users.

Such a challenge can lead to a lot of time and efficiency loss, especially for users working on tight deadlines. They can’t rely on the generative AI tool to get the right answers, as they’ll have to add multiple prompts to get the right answer.

3. Handling complex or controversial topics

This is not just for ChatGPT but AI tools that generally fail to express their thoughts on complex or controversial topics.

When asked about its belief in the theory of relativity, here’s what ChatGPT says:

The tool doesn’t indulge in affirmative answers from its end when asking about its beliefs or opinions in certain established theories. Instead, ChatGPT reinstates what is already given and fails to answer what was initially asked.

4. Addressing Incorrect Answers

Many companies believed that with ChatGPT coming to the market in full swing, the content industry may take a huge hit. And they weren’t wrong.

Studies suggest that ChatGPT crossed 1 million users in just 5 days of launch and gained 100 million active users by January 2023.

A lot of companies started generating their content through the AI tool and tried building content for their websites.

But after a few months down the line, users started to realize its downsides as well, one being its ability not to offer the right answers.

CHatGPT was undoubtedly generating content if users were adding the right prompts for it, but here’s where the tool faltered:

• It cannot generate a content piece of more than 700-800 words for now. That means companies who plan to write long-form articles have to generate content on a section basis. And even then, they cannot guarantee a flow in the content piece as human writers can.
• It offers stats but without its source. A lot of writers had to search for the source of the stats separately but sometimes realized that these stats don’t even exist.
• It even fails to offer its users up-to-date data as it was trained on the database before 2021. Even if facts have changed for a certain topic, chances are that the content generated by the tool won’t be reliable.

5. Factors That Impact Trustworthiness in AI-Generated Content

These severe issues from above lead to another challenge about the data trustworthiness from the AI tool.

ChatGPT claims that one of its biggest challenges with content generation is bias and stereotyping.

Due to the training data, there are chances that the tool might show bias in the content it shares with its users. Worse, the statements can be false, misleading the users about their validity.

Strategies for Enhancing Accuracy and Trustworthiness for FAQs

Given how severe the ChatGPT challenges are, we identified the best possible ways to enhance the accuracy and trustworthiness of your FAQs. These strategies include:

1. Pre-training with reliable data sources

When training a model, you need to ensure that you are using high-quality, diverse, and up-to-date data for its pre-training process. This will allow you to increase the data spectrum and make answers more relevant. What’s more, you can filter misleading data to prevent your model from learning incorrect or skewed information.

2. Fine-tuning with human reviewers and guidelines

Another way to ensure trustworthiness in answers is through human intervention. With human reviewers, you can ensure that the answers generated are legitimate for users. Create a set of guidelines that reviewers can use to train the model and ensure that it learns to produce more accurate responses with each training interaction.

3. Verifying answers and credibility

AI generates content based on the dataset on which it has been trained. While the data you add may be right, sometimes it may vary based on the query added by the user. In that case, you must verify the answers through manual fact-checking when testing the model. It is always better to cross-check the answers from verified sources to keep your information up-to-date.

4. Optimizing responses

Another strategy you can use is optimizing responses for your AI tool. This can be done in multiple ways. Some of these include the following.

Tailored Prompts:

Create tailored prompts or questions that you know will yield consistent and right responses from the AI tool. For this, you can train your AI model with multiple prompts, so they generate desired results.

Conditional Logic:

This is another way to optimize your ChatGPT answers hassle-free. With conditional logic, you can ensure the AI tool generates specific answers for certain user queries.

Clarity & Simplicity:

If you are not receiving clear answers from the tool, you can keep modifying your questions. This form of encouragement will help the AI tool generate clearer and more concise answers for users.

5. Identifying and addressing biased language

Signs of biased language are one of the repercussions of the initial dataset used to train the AI tool. In such a scenario, you need to:

1. Train the tool with diverse data to ensure that it avoids biases when answering a user prompt or question.
2. When you see hints of bias in ChatGPT answers, you can leave another prompt that tells that such biased language isn’t right. Understand that the tool is still in the learning stage. It is bound to make such mistakes. But with your direction, the tool can avoid such biased language in the future.

6. Customized interaction

To ensure that you receive trustworthy answers from the AI tool, make sure to have customized interactions with the tool. This is a great way to condition the AI tool to receive the right answers. Also, it helps you add a human touch to the conversation.

7. Regular training and calibration of human reviewers

Don’t let just one person be in charge of the conditioning process. Involve more human reviewers to train the AI tool regularly. You can include team members from the product team who know the product and service better. They can train the tool and input information about your product and its upcoming changes. This way, when users interact with the AI tool, they will find more information about your tool.

You can also ask your content team to interact with the tool regularly. This will ensure that the tool understands the tone and voice it must use when people ask them questions about the product.

8. Handling controversial topics

When asked ChatGPT about its downsides, here’s what it says:

“Overly verbose and generates biased content.”

These aren’t our words but what ChatGPT thinks about its performance overall. Now, imagine your customers interacting with this tool to understand your product’s downsides. What do you think the tool might say?

You need to take care of this when training with the AI tool to ensure it doesn’t generate negative reviews about your product. To handle such controversial subjects, you must train the product regularly and help its condition so that it doesn’t leave a bad impression on prospects.

9. Implementing real-time user feedback

This strategy must be implemented even if you don’t use AI integration with your tool. Consistent feedback from users can help you improve FAQs and allow you to ensure that they are helpful for users every time they have doubts about your product.

Documentation tools like Document360 allow you to capture feedback from users every time they visit a FAQ article on the help center.

You get to learn whether your articles have been helpful to users or not. Based on the feedback, you can make changes to the information and ensure it helps all users whenever they have doubts about the product.

The same can be implemented when you use ChatGPT for generating FAQ answers. Once it generates answers, it allows you to rate the experience.

Based on the feedback, you can help the AI tool understand how the answers can be improved further so that users get meaningful and right answers.

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10. Continuous Improvement and Learning

If you thought that receiving feedback from users and implementing its takeaways during the training period with the AI tool is all you have to do, we’re sorry to say that’s not true.

It needs to be conditioned consistently. That means you need to ensure that human reviewers train the tool, keep sharing feedback, and implement the right measures as per the feedback regularly without fail. This is the only way the AI tool can improve continuously and improve its results every time.

Tips for users to obtain the best answers from ChatGPT for FAQs

Now that we’ve understood a wide range of strategies one can implement to receive trustworthy answers from ChatGPT, let’s take a look at some of the useful tips that can streamline the way one writes its prompts.

Please note that these tips will prove to be beneficial for human reviewers who plan to train the AI tool regularly.

1. Providing clear and specific questions or prompts

This is undoubtedly one of the most basic yet crucial tips that people still don’t follow through properly. Explain your concerns in simple words and let the tool know how long the answer should be.

There’s a difference when you add a prompt like

When compared to a prompt below:

Did you notice? In the second prompt, we explained pain points like a lesser workforce or explaining these suggestions to other team members. Based on the prompts, the AI tool generated different answers, and the tone was also different .

Therefore, you must ensure that you provide clearer and more precise questions to receive desired answers.

2. Requesting clarification if responses are unclear

The best part about using ChatGPT is its ability to generate answers with the same intent multiple times. If you feel that the answer wasn’t clear or didn’t match the intent of your question, you can always request it to generate another response. This way you can ensure that clear answers are generated for your FAQs.

3. Verifying information from trusted sources

As mentioned above, ChatGPT users’ biggest challenge is their ability to generate bias or include wrong facts in their answers. These can leave a bad impression on users who refer to your FAQs to get their doubts cleared. That’s why you must develop a practice that helps you deliver only the right information to your customers.

Once the answers are generated, verify from the right sources to understand if the information generated is correct. Even if you condition the AI tool with regular training, there are chances that it still might generate wrong answers if it fails to understand the intent of the user’s question. So, verify the information before adding it to your FAQs.

4. Breaking down complex questions into simpler components

Another useful tip to improve the prompts for your FAQs is to break down complex questions into simpler components. Don’t expect to write multiple questions in one go; expect the tool to answer all your questions in one go. Ask one question at a time so that it gives you more accurate and clear answers hassle-free.

5. Using common language and avoiding technical jargon

You can also try avoiding complicated words that may confuse the AI and hamper its output. Avoid technical jargon and write your prompts in simple language to help ChatGPT understand the intent of your question faster.

6. Reporting issues and offering feedback to OpenAI

Make it a practice to report any issues you face with the tool and share feedback with OpenAI to help them enhance the performance of the tool. Constant feedback will allow the developers to improve performance consistently.

Case Studies and Success Stories

We’ve so far understood how ChatGPT works and how we can improve its accuracy with the right prompts and feedback to get the right answers to some of the frequently asked questions. Now, let’s take a look at some of the success stories of some of the brands that use AI to their benefit and help their users get answers faster.

Erica – Virtual Financial Assistant by Bank of America

In an attempt to help their customers find answers to their questions faster, Bank of America came up with an AI assistant solution, called Erica, to answer all commonly asked questions faster.

With Erica, the bank has successfully tackled common issues such as:

• Refunds
• Duplicate charges
• Reward points
• Recurring charges and bills
• Bill reminders
• Updates on monthly spending
• Balance updates
• Past transactions
• FICO® Score insights

These updates have helped them to reduce the frequently asked questions from customers and improve overall customer experience with the brand in terms of the services.

Ada Health GmbH – Symptom Checker

With the intent to help people understand their symptoms and identify possible reasons why they aren’t feeling well, the makers of Ada wanted to bring a revolution in the health sector with AI. This AI tool is known for successfully diagnosing possible reasons why people aren’t feeling well. It asks multiple questions and even answers frequently asked questions about health concerns. This two-way conversation helps people to diagnose their conditions and get treatment faster.

Get Accurate ChatGPT Responses From the Right Prompts

ChatGPT has surely brought a revolution in the generative AI sector. Its ability to generate answers to the questions asked by users has helped people from different sectors simplify their work in no time.

While that’s a great upside, the AI tool is also known for its lows. But to tackle them the right way, we’ve listed down 10 tried and tested strategies along with tips that will make it easier for every ChatGPT user to get accurate and trustworthy answers to their frequently asked questions.

We hope this helps!

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Documentation culture – Setting stage

The C-level executives can set the stage for documentation culture based on the organization’s vision and mission. This can also be aligned with a corporate strategic plan to get buy-in from all employees. Once a strong purpose is set for documentation to be part of all aspects of work, the cultural fabric gets woven, and middle-level managers can play their role to amplify the documentation culture.

Documentation culture ensures that documentation is adopted with rigor across the organization. No knowledge goes without being documented and stored in the organizational knowledge repository. Thus, all organizational knowledge becomes its institutional memory! This documentation extends to all practices in project management, program management, customer support, customer services, and digital products. For service organizations, documentation is all about documenting all business processes, standard operating procedures, service standards, service blueprints, and so on to achieve operational efficiencies, service consistency, and operational excellence. For product companies, documentation is all about product documentation, help center guides, troubleshooting guides, user manuals, technical documents, etc. Irrespective of the type of organization, capturing all organizational knowledge is important for business continuity and knowledge retention, which lays the foundational stone for innovation.

Also Read: The Power of Organizational Learning and Collaboration with Knowledge Management

Hiring for cultural fit

The assessment should involve ensuring cultural fit when a new employee is hired. This would ensure that new hires already possess those cultural beliefs and have inherent documentation characteristics. This would accelerate documentation adoption across all parts of the organization’s core business activities. The new hires will also play a role in the change management of existing employees in the organization in adopting documentation practices. Thus, it is vital that the Human Resources (HR) department sets standards and processes for hiring new employees based on documentation of cultural values and beliefs.

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Closing remarks

Documentation practices must be backed into all business processes and should be regarded as behavioral traits in the organization. The organization has to invest in employees’ skills, frameworks, practices, and tools to enable employees to practice documentation. Documentation culture could set the organization to future-proof against competitors. Documentation culture will lead to service excellence in service-oriented organizations and customer satisfaction in product-based companies. Let’s all embrace documentation culture!!

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