How Chalk Uses Chalk | How We Evaluate LLM Outputs Before We Trust Them

Chalk uses Agent Evals to validate LLM-generated account-memory fields, sales labels, company-fit assessments, briefs, and call summaries before those outputs are reused across workflows such as prioritization, account research, and leadership analysis. The approach turns business definitions into reviewed examples drawn from real conversations, combines structural validation, deterministic comparisons against expected labels, and rubric-based model judging, while treating automated judges as evidence rather than definitive truth. Teams compare prompt and model versions on the same examples, reserve unseen cases to test generalization, and refine rubrics when errors emerge, such as confusing marketing chatbots with action-taking agents or misrepresenting a prospect’s availability. Evals are also used to ensure sales summaries capture relevant use cases, constraints, buying risks, and next steps without inventing facts. Although evaluation cannot eliminate information loss or guarantee correctness, it helps determine whether reusable AI outputs are reliable enough for their downstream impact while preserving original source conversations for cases requiring nuance.

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