DEV Community: RAGAVI

Calling all Technical Writers!

RAGAVI — Fri, 18 Oct 2024 05:48:27 +0000

We're running a quick survey to gather your insights on the evolving role of technical writers and the impact of AI on the way we work. Your voice matters, and we’d love to hear from you! What's in it for you?

Share your thoughts on how AI is transforming technical writing
Discover emerging trends and challenges in our fast-paced industry
Help shape the future of content creation!

And the best part? Once the survey wraps up, we'll be sharing the collective insights right here in this channel – so you'll get a firsthand look at how others in the community are adapting and evolving.

Survey Link - Help Us Understand AI’s Role in Technical Writing

Thank you for helping us shape the future of technical writing! Let’s make our voices heard!

Role of Videos in Modern Technical Writing

RAGAVI — Tue, 15 Oct 2024 12:01:45 +0000

Given the speed and bandwidth of current internet infrastructure, videos are dominating.In technical writing, videos are essential to articulate complex workflows. Then, it becomes easy for your audience to register the flow and undertake the right steps to accomplish technical writers can use videos for the following scenarios

Explaining concepts
Articulating “How-to” process
Diagnosing any issues

The modern audience are very tech-savvy and aware of YouTube / Shorts / Reels. Thus, watching videos in a technical documentation site feels natural to them and prefer consumption of new knowledge in the video format.

Videos in software documentation

In software documentation, videos can be very useful in many scenarios. It is mostly used for showing audience on “how-to”. Instead of writing a step-by-step process / procedure on how to configure a particular feature, videos become powerful medium to present the same information visually. These “how-to” videos also increase product’s feature adoption.

Videos also play a huge role in helping customer support agent in delivering solutions to customer support tickets. These videos reduce the resolution time thus increasing customer satisfaction.

Tutorial videos are great for learning and developing of new skills.Tutorial videos serve as an asset to the software company in gaining traction in their ecosystem and promote inclusive learning. These tutorial videos can also be embedded into the product, thus making them an integral part of your product strategy. Videos uploaded to popular sites such as YouTube and Vimeo help to drive traffic to your website.

Videos in user manuals

User manuals are fundamental to many hardware product companies as they explain how to assemble a product, configure it, or troubleshoot any issues with it.

Most of the customers try to troubleshoot the problem using the internet. If the printed user manual is not available on the internet, then customers must call the trained technician for support. However, if the user manual is made into a video, then it becomes easy for customers to troubleshoot the problem by themselves. Any common issues with your product can be made into a video. Thus, it increases the self-service rate boosting your customer satisfaction.

Many companies, such as Ikea, Bosch, and so on, produce product assembling and troubleshooting videos. These videos are available in YouTube to help their customers’ self-serve.

Situations to use videos

Videos are a powerful medium to use as it engages all our senses. It has visual elements, sounds, and text. This makes it a powerful storytelling medium. It should be used on certain scenarios such as

Information density

It is recommended not to pack too much information into short videos as your customers might be overwhelmed by information density.
Time interval

It is recommended to create “how-to” and troubleshooting videos of length 1 – 3 minutes. Tutorial videos can be as long as 5 minutes. Instead of creating one lengthy video, break them into few short videos.
Content

Instead of showing stock image, and graphics, showing real demo would enhance content quality and increases the authenticity of the solution provided in the video

To continue reading about the role of videos in modern technical writing, Click here

Ideal Organizational Team Structure for Technical Writers

RAGAVI — Thu, 12 Sep 2024 09:39:07 +0000

Technical writers are valuable as they create new knowledge for the company. If your company is building software products, technical writers are involved in the software documentation, UX microcopy, and troubleshooting guides. If your company is building hardware products, technical writers are involved in technical documentation and compliance documentation. If your company is a service provider, technical writers write user manuals, standard operating procedures, service documentation, etc. Where do technical writers fit in the whole organizational structure? To whom should they ideally report to? More importantly, what are the roles and responsibilities of the team in terms of adding business value to the company?

The team that technical writers end up with matters as it affects how companies view technical writers. It also affects how technical writers can access resources, technology tools, and the governance process to influence key decisions in the company

Technical Writer in SaaS Product Company

Technical writers usually report to the Director, Product Manager, or Chief Product Officer via their manager. This helps technical writers collaborate more closely with developers, product managers, the QA team, and UX designers. This would aid in producing more comprehensive documentation on product features, how-to tutorials, and release notes. Working closely with developers helps technical writers understand the product feature functionality better to make more accurate documentation. Having product managers, technical writers, and UX designers report to the Director, Product Management or Chief Product Officer is advisable to have a good functioning documentation team.

Some SaaS product companies have instances where technical writers report to customer support managers/directors. Technical writers usually collaborate heavily with support agents, leading to a high volume of troubleshooting guides and amending existing articles with new content. This structure is highly beneficial for companies where the volume of support tickets is large and technical writers’ Key Performance Indicators are to increase the self-service rate.

There are a few instances where technical writers report to the Marketing manager focusing on building documentation to generate traffic. Others are more focused on SEO-related practices to generate leads/prospects through software documentation.

Nevertheless, technical writers generate immense value in creating new knowledge for SaaS products. Given the nature of SaaS products where frequent releases are required, technical writers have to be agile to produce good quality documentation in a limited time!

To continue reading about ideal organizational team structure for technical writers, Click here

LLM Agents for Technical writing

RAGAVI — Thu, 29 Aug 2024 09:37:06 +0000

New knowledge is being created rapidly due to advanced technology and increased interactions. It arises in various settings like meetings and events. Knowledge comes in different formats and can be stored in various workspaces. It has value that can be enhanced through reliable sources and offers a competitive advantage.

Current landscape

Knowledge creators are still practicing classical frameworks and processes to create new knowledge. This new knowledge comes in product documentation, user manuals, standard operating procedures, and so on within organizational settings. Knowledge creators play a support role as enablers so that organizations’ customers benefit by accomplishing tasks using the documentation.

Knowledge creation teams are often viewed as costs rather than revenue drivers. Even though a few frameworks and metrics are available to quantify the value of the knowledge creator’s team, it is harder to associate the direct evidence of activities accomplished by documentation with business outcomes metrics. At scale, some of the business value metrics justify the investment into knowledge management practices, yet many organizations are not making the right move.

The value stream of the knowledge creators ends once the customer finds a relevant knowledge base article. However, the customer value stream begins from this stage. The real outcome that the knowledge creators’ team should chase is

How does documentation help to accomplish a task?
How does documentation help customers to self-serve?

The data to calculate the above metrics needs sophisticated engineering effort to collect and curate the required data. The main drivers hindering technology adoption are hallucinations in GenAI responses and GenAI’s inability to provide a reliable approach to curating information. More importantly, technical writers are resilient to change, and legacy product vendors are slow to introduce GenAI capabilities inside their products. Technical writers undertake many activities that can be automated without much manual effort. Modern-day customers need to access information quickly and more importantly ability to accomplish tasks using the discovered knowledge even quicker! Customers are getting familiar with using ChatGPT-like interface and prefer conversational design in many products.

Drivers of change

The are three forces at play in the current market landscape that drive change in the knowledge base domain

Technology driver: Automation and intelligence will be abundant across different industries, and the cost of intelligence will go down dramatically. Given the advancements in GenAI technology, new tools are available to solve many business problems in newer ways. Knowledge Management practices are getting disrupted as old practices are either being automated with intelligence or made obsolete.
Customer behavior: The shift in customer behavior is disrupting User Experience (UX) of how knowledge will be consumed and used to produce business outcomes. Customers wish to accomplish complex tasks quickly with human-in-the-loop. Also, they want to complete simple tasks more autonomously utilizing a reliable knowledge base as the source.
Knowledge creation patterns: The way new knowledge is being created is also facing disruption as businesses are trying to reduce value lead time. The quicker the value is realized, the quicker they can capture and monetize the value. This fundamental rule is now becoming a mantra from knowledge creators. The GenAI will accelerate knowledge creation from multiple sources and many practices in knowledge management will be taken over by the GenAI tool.

Anyone who creates new knowledge and uses knowledge will want a new way of better knowledge creation process and utility of the new knowledge respectively.

To continue reading about LLM agents for technical writing, Click here

Importance of trust in adopting GenAI for knowledge base

RAGAVI — Wed, 24 Jul 2024 06:13:50 +0000

Technical writers produce a single source of truth by coordinating with Subject Matter Experts and stakeholders. They draft clear articles and remove ambiguity.They produce artifacts such as user manuals, standard operating procedures, troubleshooting guides, process manuals, and software “how-to” guides. These artifacts have absolute truth, making “trust” inherent. Some are used for compliance and regulatory filing of products in the hardware domain for manufacturing and medical devices. Thus, technical writers’ workflows are critical in ensuring accurate information in user manuals. Quality control workflows ensure that technical writers’ work is peer-reviewed and vetted by Subject Matter Experts before publication. End users of the documentation rely on a PDF or online knowledge base for accessing accurate information.

Current Trend in Search

The search engine which is based on “lexical keywords” brings in relevant articles based on user keywords. Thus, the end users always go to the article source to get the information they need.

Example of lexical search

However, things have changed after the introduction of GenAI technology such as ChatGPT, Gemini, Claude, and so on. Now, GenAI-powered search engines are taking over the semantic search.

Example of semantic search

The GenAI-based agents act as an interface between end users and documentation. Given the generative nature of this AI technology, it is harder to have control over what is generated as a response to user questions! This impacts “trust”!

Limitations of Current GenAI

The characteristic of Artificial Intelligence (AI) is that it is non-deterministic. GenAI tools are based on Large Language Models that predict the next token (token is ¾ of a word).

GenAI tools generate responses based on user prompts. The Retrieval Augmented Generation (RAG) architecture supplements the knowledge gaps in LLMs. RAG provides better context and up-to-date facts to help LLMs generate better responses. The quality of the response depends on

Quality of the prompt
Quality of the content
Underlying LLM

Quality of the prompt

The quality of the prompt determines the quality of the generated response. Suppose the prompt is vague and contains ambiguous terms. In that case, it might confuse the RAG architecture when retrieving article chunks that might have ambiguous information, leading to a non-factual response that is not grounded in truth.

To continue reading about the importance of trust in adopting GenAI for knowledge base, click here

How GenAI can improve API documentation?

RAGAVI — Tue, 16 Jul 2024 11:47:56 +0000

The API documentation is an essential toolkit for any developer utilizing your APIs to integrate with other business applications. For example, many API documentation tools offer a playground whereby developers can “Try” how APIs produce responses for a certain input through various endpoints. Some tools automatically generate code samples for various programming languages so that developers can directly plugin the code for their integration efforts. Developers also explore the various documentation related to API endpoints, such as security, path parameters, query parameters, and responses.

GenAI use cases for API Documentation

Generative AI (GenAI) enhances the developer experience by providing new ways to interact with API documentation. Developers can get code snippets quickly instead of browsing through entire documentation, troubleshoot errors effectively, generate code documentation, and more importantly, annotate custom fields inside API documentation specification files

1. Content Generation

GenAI tools can automatically create documentation for API endpoints since they can understand the code snippets' logic and functionality. This helps developers to build documentation for REST API endpoints quickly and also emerging GraphQL as well. These can be part of the developer’s IDE/SDK stack whereby documentation generation happens quickly.

2. Enhance Consistency and Quality

GenAI can read and adopt the style guides. The style guides can be then incorporated during the documentation creation process thus enhancing consistency and producing high-quality documentation.

Code snippets

Instead of browsing through the entire API documentation for code snippets, developers can use GenAI-based search engines to get a code snippet quickly by asking the right questions.For example, the GenAI technology is smart enough to convert code from one programming language to another language. Code snippets can be checked for syntax errors and logical errors. Thus, developers feel empowered as they are able to accomplish tasks much faster and more efficiently.

Troubleshooting

When a developer is faced with some errors and wishes to troubleshoot code, the GenAI chatbot can greatly enhance the developer experience. Just pasting the error output or error logs into the chatbot prompt and asking how to debug this error would produce a response detailing the troubleshooting sequence. The chatbot can guide and also educate developers on best practices. Suppose the chatbot is using any private knowledge utilizing any internal documentation that has reported this issue as a bug. In that case, it can immediately alert developers in the chat interface about the issue. If the developer is logged in, the chatbot can provide more personalized information based on historic chats, such as

Proactive updates on the issues that the developer has reported
Provide more personalized responses based on the developer profile and usage patterns
Provide updates regarding API uptime and other reliability metrics

To continue reading about how GenAI can improve API documentation, Click here

The Era of LLM Agents: Next Big Wave in Knowledge Management

RAGAVI — Fri, 12 Jul 2024 07:48:55 +0000

Gartner predicts that search engine volume will drop up to 25% in 2026. This is because of the emergence of GenAI-powered search engines. Customers prefer to use a ChatGPT-like interface to seek answers, which are powered by Large Language Models (LLMs) for their convenience and ease of use. Eventually, customers will abandon search engines! Many companies utilize GenAI-based agents such as chatbots, and assistive search to provide rich customer experience.

The appetite for these GenAI tools is growing, and many people are adopting these technologies faster. At Document360, we have adopted GenAI capabilities such as Eddy AI to provide enhanced search experience and content tools to help technical writers increase their productivity.

Evolution of GenAI Search & Chatbots

The Retrieval Augmented Generation (RAG) approach underpins how the GenAI search engine works. The need for a conversational interface leads to many companies adopting open-source frameworks such as LangChain. Both GenAI-powered search engines and chatbots are gaining a lot of traction amongst the new wave of customers who are tech-savvy and want to get things done quicker! benefits of these tools are multi-fold such as

Helps to learn better through iterative probing
Assists in producing accurate answers to customer questions
Feels natural to have a conversation

The enterprise has to empower those customers looking to resolve their queries themselves with the right tools such as a chatbot. GenAI search & Chatbots are predominantly used by enterprise organizations in deflecting support tickets. Many enterprises are finding success in increased customer satisfaction scores after the introduction of chatbots inside their products and information portals.

Beyond AI LLM Agents Will Shape the Future

The LLM agents are poised to become the next big thing. These agents may be tasked to involve human beings before making decisions, thus human-in-the-loop principles should be applied while designing these systems. The future is exciting given the rise of LLM agents who are programmed to perform tasks, make decisions, and communicate with other LLM agents for information exchange. They can leverage collaborative intelligence to undertake sophisticated tasks quickly and easily.

Imagine asking a GenAI-powered search engine a “How-to” procedure. For example, in docs.document360.com, you can ask Eddy AI, “How to insert a business glossary term into an article”

Eddy AI produces four sequential steps to accomplish this task, which may or may not need human input. If a GenAI-based search produces these steps, an LLM agent can understand and execute them inside the product. This can be accomplished in two ways.

To continue reading about the era of LLM Agents, the next big wave in knowledge management, Click here.

Refactoring content for GenAI readiness: Best Practices and Guidelines

RAGAVI — Mon, 08 Jul 2024 10:45:38 +0000

Refactoring content is a must, given the proliferation of GenAI tools in the market. Most of the GenAI vendors have scrapped the internet to train their Large Language Model (LLM).Public knowledge vendors likely already use public GenAI bases, and customers may turn to tools like ChatGPT for answers.

If you have implemented a GenAI-powered search engine on top of your knowledge base, refactor content for GenAI agents, considering both human readers and GenAI needs.

This blog provides practical guidelines for undertaking content audits to refactor the content suitable for GenAI-based agents and tips on balancing the needs of human readers and GenAI agents. Prioritize public-facing knowledge bases for content audits to ensure customer satisfaction.

GenAI Knowledge Management Strategy

RAGAVI — Mon, 01 Jul 2024 05:13:47 +0000

Knowledge management is strategically important to ensure that organizations can innovate by utilizing institutional memory and preserving customer loyalty. About 80% of organizational knowledge is unstructured and available in text, videos, images, and so on. Organizations that fail to mobilize their knowledge assets are heading toward business closure!

Given the proliferation of GenAI technology, it is prime time for many organizations to put forward a stronger culture of knowledge creation and sharing to stimulate business growth.

In this blog, we shall cover motivations for a new knowledge management strategy, a playbook to create a modern knowledge management strategy along with business use cases, and how to execute it with the strong cultural ethos of the organization.

Motivations for New Knowledge Management Strategy

GenAI technology has changed the way new knowledge is created, how it is been shared, and more importantly how it is consumed for strategic and tactical business advantage. GenAI technology is helping many internal stakeholders to create, organize, and share knowledge seamlessly within their organizations.

GenAI technology can be viewed as a strategic investment in helping organizations mobilize internal knowledge spanning across a multitude of business systems in various formats! More importantly, any new knowledge that is created is attached to many metadata, it helps stakeholders understand the importance and nuances of using the specific knowledge for their use cases.

Business Use Cases of GenAI Knowledge Management

There are several business use cases where GenAI technology can applied effectively for knowledge creation, organizing knowledge, and knowledge dissemination. GenAI capabilities can be tapped into for organization-wide knowledge searches that can index and source knowledge across different knowledge repositories.

Use case #1 – Meetings & Email

Meetings help organizations get clarity on issues, help to make decisions, talk about risks, and so on.These meetings can be transcribed into textual form using GenAI technology and curated to form baseline knowledge on topics of interest.

The typical use case where organizations see value is through automatically curated Minutes of Meetings (MoM) and action items. GenAI technology can also assign action items to the right set of stakeholders based on additional data such as contact lists, meeting invitees, and so on.

Moreover, learnings from each strategic program and project can be made available to GenAI capabilities so that any internal stakeholders can query and consume the knowledge without any friction to avoid making wrong decisions.

To continue reading about GenAI knowledge management strategy,Click here

How Does GenAI Powered Search Engine Work?

RAGAVI — Wed, 26 Jun 2024 07:31:15 +0000

Generative Artificial Intelligence (GenAI) technology is setting a new trend in how information is being consumed in this AI-first era. It is built on top of Large Language Models (LLMs), which play a huge role in building an assistive search engine that is powered by GenAI capabilities.

The problem with LLMs is that they cannot provide any recent or present information as they need months to retrain with new data. To overcome this limitation, an innovative architecture is proposed that sits on top of LLMs.

What is the Retrieval Augmented Generation (RAG) framework?

The Retrieval Augmented Generation (RAG) is an elegant way to augment recent or new information to be presented to the underlying LLMs such that it can understand the question that seeks new information. The RAG framework powers all the GenAI-based search engines or any search engine that provides context-aware answers to customers’ questions.

RAG architecture

The RAG architecture consists of a Retriever Module and a Generator Module. For RAG architecture to work, we need to chunk all the knowledge base content into small chunks. There are many ways to chunk all the knowledge base content, such as

Chunk them based on content hierarchy
Chunk them based on the use case
Chunk them based on content type and use case

Once the text data is chunked, then all these chunks need to be converted into text embedding. A plethora of APIs are available from GenAI tool vendors whereby the embedding model is a popular API quickly and cheaply. OpenAI Ada text embedding model is a popular API that is widely used.

The next step in the process is to store all text embeddings along with their related chunks and metadata in a vector database.

To continue reading about how GenAI powered search engine work? Click here

How to Optimize Content Using GenAI Powered Search Analytics?

RAGAVI — Fri, 21 Jun 2024 12:36:37 +0000

Technical writers have relied on “lexical search” analytics regarding what keywords have been typed in the search engine on their knowledge base site for analysis. The typical category of analytics includes article performance, search analytics, feedback, and reports for technical writers on their performance.

Analytics helped technical writers enhance content engagement and user journeys to optimize the knowledge base content continuously. This increased the self-service rate and significantly reducing support tickets.

When optimizing knowledge base content using gen AI-powered search, you have to consider two types of analytics: normal keyword search and prompt-based search.

Keyword-Based Analytics Vs. Prompt-Based Analytics

Given the proliferation of GenAI technology, many organizations have deployed ChatGPT-like search on their knowledge base. Customers prefer this over keyword-based search. The tables below show the nature of lexical keyword and prompt-based analytics.

Factors to Consider in Prompt-Based Analytics

Here are the factors you need to look for when enhancing content engagement and user journeys using prompt-based analytics

Prompt Analysis

Technical writers can access the list of questions (prompts) that have been raised by their customers which gives them better clarity on

What kind of questions that my customers type in
What are the key business keywords that are often used by your customers, that relate to your business glossary?
What types of questions are commonly asked, and what types of similar question
Why are those questions being typed in, and how do they correlate with other business activities
What information is most commonly been sought

This helps technical writers better understand the customers’ intent, leading to better business outcomes.

To continue reading about how to optimize content using GenAI-powered search analytics? Click here

How to evaluate GenAI-based assistive search responses?

RAGAVI — Fri, 14 Jun 2024 12:10:15 +0000

Many organizations around the world are adopting GenAI technologies in their workflow to make their teams more productive and to achieve business outcomes that drive business growth.

Technical writers have a huge role in the GenAI era in ensuring trust in GenAI system-generated responses. Technical writers can produce GenAI-friendly content, help train the GenAI systems to produce the right responses based on human feedback, and also evaluate the responses of the GenAI system before deploying it in the production environment.

Things to Consider in Evaluating GenAI Responses

1. Relevancy

The GenAI-generated response should be relevant to the customers’ questions/prompts. The generated response will be relevant if the underlying retrieved mechanism retrieves relevant chunks from the knowledge base. Thus, it is important to look at evaluation metrics about relevancy

2. Accuracy

Trust is fundamental in ensuring the adoption of GenAI-based agents. Accuracy plays a crucial role in evaluating the GenAI response. Accuracy metrics can be computed by comparing the GenAI response with the ground truth

3. User Feedback

User feedback plays another important role in trust. If GenAI responses are not relevant or non-factual, users can flag them for inaccuracy. This should be considered to retrain the GenAI-based agent to produce accurate responses over time

4. Error Handling

If GenAI responses cannot be generated, the response should be courteous

5. Response Time

User experience is affected by response time. If the response time is longer, then the user has to wait and they might abandon using GenAI-based agents. A typical balance has to be attained between user experience, cost, and accuracy.

Framework to Evaluate GenAI Responses

Technical writers are best suited to evaluate the responses generated by GenAI-based assistive search as they curate accurate information across the organization and interact with many subject matter experts. The responses from GenAI-based assistive search are very subjective; thus, it is important to create some baseline around GenAI-based assistive search responses through some numerical metrics.

These metrics can guide improvising responses by either tweaking the underlying content or tweaking the GenAI-based assistive search tool’s functional parameters, such as system messages, chunk size, etc.

Two open-source frameworks are available to evaluate the responses generated by GenAI-based assistive search.

To continue reading about how to evaluate GenAI-based assistive search responses? Click here