Prompt Optimization

Prompt optimization is the practice of systematically improving how instructions are written for LLMs to get better results with less cost.

It ranges from manual trial-and-error refinement to automated frameworks that treat prompts as learnable parameters. Techniques include few-shot example selection, chain-of-thought structuring, and prompt compression to reduce token usage. Also known as: Prompt Tuning, Prompt Refinement

What this topic covers

Foundations — Prompt optimization treats instructions to language models as tunable parameters rather than fixed text.
Implementation — The guides here cover building automated prompt optimization pipelines, selecting frameworks for your use case, and managing the cost-quality tradeoffs that arise when compressing prompts for production.
What's changing — Prompt optimization is shifting from manual craft to automated infrastructure as frameworks mature and cost pressures push teams to compress context.
Risks & limits — Automated prompt optimization can encode biases or optimize for proxy metrics that diverge from intended behavior.

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