Prompting vs. RAG vs. Fine-tuning

Key idea: A better prompt fixes rules you can write down, RAG fixes facts the model can't know, and fine-tuning teaches patterns from examples but bakes in old facts. Most real products combine them.

Which approach fixes which problem

Run an approach to add it here

  1. Run Plain. Which kinds of job does it get wrong?
  2. Run Better prompt. What did instructions fix, and what couldn't they fix?
  3. Run RAG. Why do the facts come right while nothing else changes?
  4. Run Fine-tuned. It knows the squads, but look at its prices. Where did they come from?
  5. Run All three. If you could ship only one approach, which would it be, and what would you give up?
Prompt rulesoffDocs (RAG)offPast examplesoffTask12 support jobsModeldoes the jobAnswerwhat it wroteCheckerright or wrong?Scorecardscore per kind
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Results

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Behind the scenes

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