PowerUp Coaching — Living Knowledge Base

Stop Storing Answers. Store the Way You Get to Them.

5 min read

Topic

Why the right retrieval target for a personal knowledge base is a procedure rather than a passage — and what that does to your model bill.

Target Reader

A knowledge entrepreneur who has built or is building a second brain, knowledge base, or skills library and expects AI to make it useful. They’ve heard the RAG arguments, may have a vector store, and are quietly disappointed that retrieval gives them their own old notes back rather than good new work.

The Fear / Frustration / Want / Aspiration

The frustration is a system that finds things but doesn’t do anything — years of accumulated material producing search results instead of judgment. The aspiration underneath is bigger and rarely said out loud: they want the AI to work the way they work, not just to know what they know.

Before State

Optimizes retrieval for finding the right answer faster — better chunking, better embeddings, arguments about semantic versus keyword. Treats accumulated documents as the asset. Assumes better output requires a better (more expensive) model.

After State

Sees an answer as a dead artifact: correct for one question, at one moment, telling the model nothing about what to do next. Points retrieval at process files instead — the steps they take to reach a conclusion. Understands the economic consequence: whatever reasoning the model lacks can be supplied from the file, which changes which model they need.

Narrative Arc

Mid-conversation about RAG architectures, Lou follows a thread back to how reasoning models were actually distilled — the training data wasn’t answers, it was reasoning traces, which is also why the labs stopped letting you download a chain of thought. The turn: a skills library is already a corpus of reasoning traces, just not wired up as a retrieval target. The resolution reframes the whole exercise — make the library the knowledge base, so a query returns the procedure rather than the passage, and the model’s own reasoning ceiling stops being the binding constraint.

Core Argument

Your knowledge base should retrieve the process for producing an answer, not the answer itself — because reasoning generalizes to the next question and answers don’t, and because reasoning stored in files can be supplied to a cheaper model instead of purchased again from an expensive one.

Key Evidence / Examples

  • The distillation observation: reasoning models were trained on traces, not outputs — “they were not asking Claude to produce an answer, they were asking Claude to produce the reasoning as output” — and the labs subsequently stopped exposing chain-of-thought. (verify: confirm the public record on downloadable reasoning traces before publication — the claim is load-bearing and was recalled from memory in session.)
  • Lou’s consequence, stated plainly: “now you could have a lower quality model, because all the reasoning’s embedded into your markdown files.”
  • The fine-tuning contrast from the same session: fine-tune on outputs and the model returns the training data. Same failure, same cause — training on products instead of processes.
  • The architectural simplification: a routing table from problem shapes to procedures may replace the vector store entirely. Nothing to embed.
  • Jay Drobez’s counter-evidence, which the piece should carry honestly: lower models do well on deterministic operations and semantic retrieval, but “when there’s a complex process that needs an orchestrator point of view, that would sometimes lose that edge.”

Proposed Structure (5–7 beats)

  1. Open on the disappointment: a knowledge base that returns your own notes when you wanted judgment.
  2. Name the misdirected optimization — everyone is tuning retrieval to find answers faster, and an answer is a dead artifact.
  3. The distillation detour: how reasoning models were actually built, and what that implies about which data is valuable.
  4. The turn: your skills library is already a reasoning corpus. Point retrieval at it.
  5. The economics — reasoning in files means the model doesn’t have to supply it, which is a different purchase decision than “buy the smarter model.”
  6. Carry the objection: Jay’s orchestration ceiling. Say plainly that this is a hypothesis Lou intends to test, not a proven pattern.
  7. Close on the reframe: the question stops being what do I know and becomes how do I decide — and only the second one is worth writing down.

Editorial Notes

This is the strongest idea from the session and the least proven — the draft must hold both. Lou said “I’m kind of hypothesizing this”, and the piece is more credible, not less, if it says so and shows the test rather than claiming the result. Overlaps with Brief - Use Your Smartest Model to Make Your Cheapest Model Smart: that one is about pre-computing a prompt, this is about retrieving a procedure at query time. Keep the distinction explicit or the two will read as the same article. The DeepSeek/chain-of-trace claim needs a real citation before publication — it is doing load-bearing work in the argument and cannot ship on recall. Concrete beats abstract throughout: one real process file, shown, is worth three paragraphs of architecture.

Next Step

  • Approved for drafting
  • Needs revision
  • Deprioritised

Graph View