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Optimizing LLM Accuracy

A strategic guide for improving model performance through prompt engineering, retrieval-augmented generation (RAG), and fine-tuning, including decision frameworks for selecting optimization methods.

Import to Prompt Buddy

Derived skill

Files assembled from official documentation

Viewing SKILL.md

Optimizing LLM Accuracy

A strategic guide for improving model performance through prompt engineering, retrieval-augmented generation (RAG), and fine-tuning, including decision frameworks for selecting optimization methods.

When To Use

Use when you need to decide between prompt engineering, RAG, or fine-tuning to improve model correctness and consistent behavior for a specific production use case.

Reference Files

FileContainsUse For
SKILL.mdEntry point: scope, routing table, and workflow.Start here.
docs/optimizing-llm-accuracy-workflow-guide.mdA guide detailing strategies and techniques for maximizing correctness and consistent behavior when working with large language models.Questions about a guide detailing strategies and techniques for maximizing correctness and consistent behavior when working with larg...
examples/optimizing-llm-accuracy-openai-optimizing-llm-accuracy-few-shot-chat.examplechatA chat-based few-shot prompting example demonstrating how to provide context and examples to improve model accuracy for Icelandic grammar correction.Exact payloads, commands, or snippets shown in A chat-based few-shot prompting example demonstrating how to provide context and examples to improve model accuracy f...
examples/optimizing-llm-accuracy-openai-optimizing-llm-accuracy-training-chat.examplechatA single training example demonstrating a system prompt and user-assistant interaction for correcting Icelandic sentence errors.Exact payloads, commands, or snippets shown in A single training example demonstrating a system prompt and user-assistant interaction for correcting Icelandic sente...

What This Skill Covers

  • We've worked with many developers across both start-ups and enterprises, and the reason optimization is hard consistently boils down to these reasons:
  • Main sections: How to maximize correctness and consistent behavior when working with LLMs, LLM optimization context, Prompt engineering, Optimization, Evaluation.

Workflow

  1. Open the most relevant file under docs/ for the exact documented workflow and wording.
  2. Open schemas/ files for exact structured contracts.
  3. Open examples/ files for concrete requests, commands, snippets, and manifests.
  4. Do not add behavior or configuration that is not present in the attached source files.

Canonical source: https://developers.openai.com/api/docs/guides/optimizing-llm-accuracy.md