Documentation

If you want to make a good RAG tool that uses your documentation, you should start by making a search engine over those documents that would be good enough for a human to use themselves. This is exactly what I’ve been trying to communicate in my org in the past few months. It’s 2024 and we still can’t have a proper search engine in organizations to find relevant information from various sources. While this problem remains to be solved, organizations are adapting RAG and AI into their tooling, but are missing the important R of the RAG: Retrieval. I’ve been an advocate of prioritizing search engines over any AI related tool in the past few months, and I found it refreshing to read about this somewhere else: ...

Many companies are integrating LLM question answering tools into their DevEx toolchain. If you’re writing documentation and you’d like to assist these tools to serve people with proper responses to the questions related to what you own, kapa.ai has a few practical tips on optimizing the technical docs for LLMs: A clear hierarchy of headings and subheadings on a page helps LLMs understand the relationships between different sections of your documentation. Troubleshooting sections formatted as Q&A are an effective source for LLMs as they mirror the questions users often ask, making it easier for LLMs to understand and respond to similar questions. Including small, self-standing code snippets can be helpful, especially for products that rely on large and often complex SDKs or APIs. Have a brief description above the code to clarify its purpose and usage. Include comments within the code to explain the logic and functionality. Keep relevant content directly in your docs rather than in linked files such as PDFs, as LLMs have a harder time parsing these. Ensure information conveyed through screenshots is also described in text, as LLMs parse text more efficiently. Clarify all acronyms and specialized terminology within your documentation to aid LLM comprehension. source ...