Max Buckley, a Google expert in LLM experimentation, dives into the hidden dangers of poor documentation in RAG systems. He explains how even one ambiguous sentence can skew an entire knowledge base. Max emphasizes the challenge of identifying such "documentation poisons" and discusses the importance of multiple feedback loops for quality control. He highlights unique linguistic ecosystems in large organizations and shares insights on enhancing documentation clarity and consistency to improve AI outputs.
46:36
forum Ask episode
web_stories AI Snips
view_agenda Chapters
auto_awesome Transcript
info_circle Episode notes
volunteer_activism ADVICE
Contextualize Chunks Before Embedding
Contextualize chunks by adding document-level context before embedding or search.
Use summaries, hypothetical questions, or cached document context to improve retrieval relevance.
insights INSIGHT
RAG Reveals Hidden Doc Problems
RAG unlocks internal knowledge but exposes many latent documentation quality issues.
Contradictions, temporal drift, and missing context are common and degrade LLM answers.
volunteer_activism ADVICE
Use LLMs To Find And Triage Problems
Use LLMs to scan batches of docs for ambiguities, contradictions, and missing explanations.
Triage results and apply quick edits or file targeted updates with subject experts.
Get the Snipd Podcast app to discover more snips from this episode
Documentation quality is the silent killer of RAG systems. A single ambiguous sentence might corrupt an entire set of responses. But the hardest part isn't fixing errors - it's finding them.
Today we are talking to Max Buckley on how to find and fix these errors.
Max works at Google and has built a lot of interesting experiments with LLMs on using them to improve knowledge bases for generation.
We talk about identifying ambiguities, fixing errors, creating improvement loops in the documents and a lot more.
Some Insights:
A single ambiguous sentence can systematically corrupt an entire knowledge base's responses. Fixing these "documentation poisons" often requires minimal changes but identifying them is challenging.
Large organizations develop their own linguistic ecosystems that evolve over time. This creates unique challenges for both embedding models and retrieval systems that need to bridge external and internal vocabularies.
Multiple feedback loops are crucial - expert testing, user feedback, and system monitoring each catch different types of issues.
Max Buckley: (All opinions are his own and not of Google)
00:00 Understanding LLM Hallucinations 00:02 Challenges with Temporal Inconsistencies 00:43 Issues with Document Structure and Terminology 01:05 Introduction to Retrieval Augmented Generation (RAG) 01:49 Interview with Max Buckley 02:27 Anthropic's Approach to Document Chunking 02:55 Contextualizing Chunks for Better Retrieval 06:29 Challenges in Chunking and Search 07:35 LLMs in Internal Knowledge Management 08:45 Identifying and Fixing Documentation Errors 10:58 Using LLMs for Error Detection 15:35 Improving Documentation with User Feedback 24:42 Running Processes on Retrieved Context 25:19 Challenges of Terminology Consistency 26:07 Handling Definitions and Glossaries 30:10 Addressing Context Misinterpretation 31:13 Improving Documentation Quality 36:00 Future of AI and Search Technologies 42:29 Ensuring Documentation Readiness for AI
How AI Is Built