Prompt optimization is equal parts craft and measurement. The techniques below improve reliability and cost - but only if you verify them with evaluation instead of vibes.
Techniques that work
- Write a strong system message. Establish role, constraints, and output format up front. "You are an expert code reviewer" beats "help me with code."
- Use variables instead of hard-coding. Templating a prompt (
{{language}},{{code}}) keeps one prompt reusable across many inputs and makes it testable. - Provide few-shot examples. Two or three input→output pairs do more to shape behavior than a paragraph of instructions.
- Use chain-of-thought deliberately. Asking the model to reason step by step improves complex tasks but costs tokens - measure the trade-off.
- Right-size the context. Too much irrelevant context raises cost and latency and can degrade quality. Trim aggressively.
Optimize for tokens, not just quality
Prompt optimization isn't only about better answers - it's about cheaper ones. Trimming a bloated system prompt can cut response cost dramatically. Watch prompt tokens vs. completion tokens to spot waste. Infere's observability surfaces token counts per request so you can see exactly where cost comes from.
Measure every change
The only way to know a prompt is better is to score it. Run your candidate against the current prompt on the same eval dataset and compare:
- Score both prompts with AI judges over a fixed dataset.
- Compare quality, groundedness, and cost per request.
- Ship the winner and roll back if online evals regress.
Prompt optimization that isn't measured is just guesswork with extra steps.
The schema of a good prompt
---
name: code_reviewer
model: auto (balanced)
variables: [language, code]
---
You are an expert {{language}} code reviewer.
Analyze for bugs, security, performance, and best practices.
Be specific and provide improved snippets.
{{code}}Next steps
Infere gives you prompt management, a prompt playground to iterate, and LLM evals to verify each change. Optimize with confidence - every tweak tested before it ships.
Every optimization changes the token footprint - see the true cost of agentic coding for how output length and model routing affect the bottom line. For versioning and deploying prompt changes, read our guide to prompt management as code. To test prompts against live models before shipping, try our AI prompt playground.