LLMs are behind today’s top tools — from chatbots to code assistants to healthcare apps. But building reliable, safe systems — especially in the enterprise environment — requires more than just great prompts.
This guide introduces the essentials of LLM evaluation — before launch and in production. You'll also learn about techniques like red-teaming and observability to make your systems more trustworthy.
What we will cover:
How evaluating LLM systems differs from model benchmarking
Key evaluation methods — human, automated, and hybrid
When to evaluate — prototyping, testing, and live monitoring
This guide is for GenAI leaders and anyone involved with building or deploying LLM systems — from governance teams to data scientists — who want a clear, non-technical intro to LLM evaluations.
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