prompt-engineer

To reduce token usage and latency, you optimize LLM prompts by pruning redundant context, selecting effective patterns like few-shot prompting, and constraining output formats. This maintains generation quality while lowering operational costs.

Prompt versioning manages catalogs with drift monitoring, while A/B testing compares variations using significance checks. Together they enable production LLM evaluation by tracking accuracy, consistency, token, and latency metrics across versions.

You design safety constraints for structured LLM generation by defining compliance requirements during analysis and applying structured safety approaches with verification. This ensures safety-critical generation meets required constraints.

Yes, you can apply few-shot prompting and tool-using loops for multi-step agent workflows. Prompt design incorporates these patterns to achieve measurable quality while managing latency and token cost.

For production prompt evaluation, you should define and track accuracy, consistency, token usage, and latency metrics. These are measured via A/B testing with significance checks to verify quality and cost targets.