Prompt Engine Optimizations

Optimizing prompts is about getting the best quality for the least cost and lowest variability.

Cost reduction tips:

• Trim context: only send the facts the model needs for this request.
• Template compression: keep long static instructions on the server and send a short reference or summary.
• Cache repeated results when possible to save calls and tokens.

Reliability tips:

• Lower temperature and add clear constraints when you need consistent outputs.
• Ask the model to return structured data (JSON) and validate it with a schema.
• Use validators and small deterministic code steps to correct simple errors.

Advanced patterns:

• Retrieval-augmented prompts: fetch relevant documents from an index and include only the top-k snippets.
• Example compression: store many examples in an embedding store and retrieve only the most relevant few-shot examples.

Measure what matters:

• Track tokens per request, success rate (passes validation), and latency.
• Run A/B tests to compare prompt variants on both cost and quality metrics.

These practices help you scale prompts without surprising costs or flaky outputs.