Original Insight
“RAG is going to retrieve chunks of content that have similar meaning to your query… But it doesn’t have the entire library’s worth of analysis or context at that time. You’ve got to think of it not as answers, but as raw material for your inference to perform.” — Lou
Expanded Synthesis
One of the most common and costly mistakes in AI implementation is expecting the retrieval system to do the reasoning. Lou’s August 14 session surfaced this as both a technical and a philosophical problem — and the coaching implications reach far beyond AI.
RAG (Retrieval-Augmented Generation) is not a question-answering system. It is a context-provisioning system. It finds pieces of your stored knowledge that are relevant to the query, hands those pieces to the inference engine, and then the inference engine reasons from that raw material. When people build RAG systems and expect them to be comprehensive, to catch every reference, to understand relational context across thousands of documents — they are asking the retrieval layer to do the reasoning layer’s job.
This session mapped out the specific failure mode in detail: the legal client wanted the AI to scan every deposition statement against every other deposition statement — a relational, comprehensive analysis. RAG, by design, returns the top 5-10 semantically similar chunks. If the answer lives in the relationship between 40 different documents, RAG will miss it. Not because it’s broken, but because that’s not what it was built to do.
Lou’s response is instructive: don’t fight the architecture. Match the architecture to the use case. Different retrieval problems require different architectures:
- Naive RAG — best for “find the most relevant answer” queries where isolated chunks carry full meaning
- Contextual RAG — best when you need the full document or process retrieved (adds document summaries to each chunk so relevance scores match holistically)
- Hybrid RAG + Knowledge Graphs — best for relational queries: “what did this person say across 40 documents, and how did their position change over time?”
The coaching principle here is about matching the tool to the task — and knowing the real task. High-performers frequently apply general-purpose frameworks to specialized problems because they haven’t diagnosed the actual structure of the problem. They use their best brainstorming process for what is actually an execution problem. They apply a mindset framework to what is actually a resource allocation problem. The dissonance produces effort without resolution.
Lou’s live debugging session with the client — where the very first question the client asked caused the system to fail — is a masterclass in the gap between builder’s context and user’s context. Lou knew the technical design of the system. The client knew his actual workflow. The collision produced the real requirements. This is true of every coaching engagement: the problem the client presents is rarely the problem the client has. The first session is a retrieval test. The sessions that follow are where the real architecture gets built.
There is also a depth insight about chunk sizing that maps beautifully to knowledge work. Lou explained that the right chunk size depends on the document type and the use case:
- Legal contracts need small, clean chunks (one clause = one chunk) to preserve signal and eliminate noise
- Deposition transcripts need larger chunks to preserve pronoun context (“he said that” — you need to know what “that” was)
The coaching equivalent: context determines what unit of information is most useful. In some coaching conversations, a single question is the right “chunk” — clean, isolated, answered on its own. In others, the full narrative arc of a client’s story is the chunk — pulling out one sentence loses the meaning. Skilled coaches know which mode they’re in.
Practical Application for PowerUp Clients
The Architecture Matching Exercise
Before building any knowledge management system — AI or human — run through this diagnostic:
Step 1: What kind of queries will this system need to answer?
- “Find the most relevant piece” → Naive RAG is fine
- “Give me the full process/steps/framework” → Contextual RAG needed
- “Show me patterns across many documents/conversations” → Knowledge graph or structured index needed
- “Synthesize everything and give me conclusions” → Context window AI (not RAG at all)
Step 2: What is the unit of meaning in my knowledge base?
- Can a single paragraph carry full meaning? → Small chunks
- Does meaning depend on surrounding context? → Large chunks or full-document retrieval
- Does meaning live in relationships between multiple documents? → Graph structure
Step 3: Am I designing for my mental model or for the user’s actual workflow?
- What will the actual user ask? (Not what should they ask — what will they ask?)
- What failure mode will happen first?
- What feedback loop is built in to catch it early?
For Coaching Clients — The “Raw Material vs. Answer” Reframe: When clients come to coaching presenting a decision already mostly made, ask: “Is that a conclusion, or is that a chunk of raw material?” Help them see that the first answer they surface is often just the most semantically similar result to the question they asked — not necessarily the right one.
Journal Prompt: “What system in my life or business am I asking to give me answers when it was really only designed to give me raw material? What would I do differently if I accepted that distinction?”
Additional Resources
- Building a Second Brain by Tiago Forte — on designing knowledge systems for actual retrieval
- The Checklist Manifesto by Atul Gawande — on designing processes that match the actual task structure
- Insight - Multi-Pass Retrieval Turns Shallow Searches Into Strategic Intelligence — on improving retrieval quality before architecture changes
- Insight - The Model Underneath Is the Multiplier, Not the Interface — on why infrastructure improvements have limits
Evolution Across Sessions
The Aug 7 session introduced the multi-pass retrieval insight — improve what you ask for before changing the architecture. This session takes the next step: when the retrieval pattern itself is wrong for the task, prompting improvements won’t fix it. You need to change the architecture. This is a natural progression: optimize within the system, then redesign the system. The Aug 28 session then covers the practical side of this — surveying what open-source tools are available for each architectural choice.
Next Actions
- For me (Lou): Create a one-page “RAG Architecture Selector” guide for coaching clients who are building AI knowledge systems — a simple decision tree matching use case to architecture type.
- For clients: When evaluating any knowledge management tool (AI or otherwise), ask first: “What is the actual query I need to run?” before choosing the storage architecture.