Guide

Fine Tuning OpenAI Models: Best Practices

Parlance Labs · parlance-labs.com

Practitioner war stories on when fine-tuning paid off and when it was resume-driven engineering.

Open on parlance-labs.com ↗

The question it answers

  1. Prompt engineering, RAG, or fine-tuning: which one does my product actually need?

Read with it

  1. A PM-friendly decision guide with real cost and time estimates for each rung of the ladder. news.aakashg.com Article RAG vs Fine-tuning vs Prompt Engineering: The Complete Guide Aakash Gupta (Product Growth)
  2. A neutral, definition-first explainer you can hand to any teammate. ibm.com Article RAG vs fine-tuning vs prompt engineering IBM
  3. The lab's own framing of the successor skill to prompting: keep context informative yet tight. anthropic.com Article Effective context engineering for AI agents Anthropic
  4. The complementary view from the biggest orchestration framework, with concrete patterns. langchain.com Article Context Engineering for Agents LangChain
  5. Runnable code for the context techniques the essays describe. platform.claude.com Guide Context engineering: memory, compaction, and tool clearing (Claude Cookbook) Anthropic
  6. OpenAI's own flywheel: evals feed prompts, prompts feed fine-tuning data, in that order. developers.openai.com Guide Model optimization (OpenAI docs) OpenAI
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