building-with-llms

Plan prompt engineering and evaluation by generating a structured LLM Build Pack that covers feature briefs, system design, prompt contracts, and evaluation strategies. This framework ensures your LLM application is tested for accuracy before launch.

An LLM Build Pack is a comprehensive plan for developing and shipping LLM-powered features. You need it when transitioning an AI copilot or RAG system from prototype to production to ensure safety, reliability, and cost-effectiveness.

Design tool contracts and system prompts by generating a detailed plan that defines tool schemas, establishes safety guardrails, and outlines monitoring strategies. This structured approach ensures tool-using agents operate reliably within defined architectural boundaries.

Yes, you can use this framework to build GPT applications and RAG features for a CRM. It generates a specific plan including system prompts, tool definitions, an evaluation set to test accuracy, and a live monitoring strategy.

Ensure LLM safety and reliability by using a launch checklist that validates prompt contracts, data evaluation plans, and safety considerations. This production readiness step verifies that features are cost-effective and safe for deployment.

Yes, LLMOps evaluation planning covers monitoring strategies for live AI features by defining an evaluation set and launch checklists. This ensures ongoing accuracy, safety, and cost management after the LLM application goes live.