Building AI Evals

For AI Product Managers, Evaluation is the most critical phase. It moves you from "It feels right" to "It scores 95%". The industry standard is the LLM-as-a-Judge pattern.

The Evaluation Pipeline

Instead of humans manually reviewing logs, we build an automated pipeline. A second, smarter model (the Judge) scores the output of your product model.

graph LR Input[User Input] --> Model[Your LLM Product] Context[Retrieval / Rules] --> Model Model --> Output[Generated Response] Output --> Judge{LLM Judge} Context --> Judge Criteria[Eval Rubric] --> Judge Judge -- Reasoning --> Score[Final Score]

Real World Case Studies

RAG Bot (Support)

Metric: Faithfulness

Checking if the answer is supported by the retrieved context (Project Grounding).

User Input

"Can I return a laptop I bought yesterday?"

Context / Rules

Policy: Electronics can be returned within 14 days if unopened. A 15% restocking fee applies to opened items.

Model Output (To be Evaluated)

"Yes, you can return it within 30 days for a full refund."

Judge Verdict

"FAIL: The model stated '30 days' but the context says '14 days'. It also missed the restocking fee condition."

Final Score:0/1 (Hallucination)

Professional Tip: The "Golden Dataset"

To ship with confidence, you need a set of 50-100 examples defined before you code. This dataset acts as your "Unit Tests" for AI quality.

PreviousInference

NextAI/ML Quiz