Evaluation (Evals)
Systematic measurement of LLM and agent quality — the production discipline that distinguishes engineering from prompting.
What is Evaluation (Evals)?
Evals are how you know whether your Gen AI system is getting better or worse over time. Without them, prompt changes are pure vibes — you fix one example and silently break ten others. With them, you ship with confidence.
A typical eval suite has: **golden datasets** (curated input → expected output pairs), **automated metrics** (exact match, BLEU, embedding similarity), **LLM-as-judge** (a stronger model rates outputs), **A/B comparison** (does version B beat version A on this dataset?), **regression tests** (does this change break previously-working examples?).
Tools: LangSmith (LangChain), Braintrust, Promptfoo, OpenAI Evals, Phoenix. Or custom — many Indian production teams roll their own. The discipline matters more than the tool.
Evals separate Gen AI engineers from prompt tinkerers. Every Indian senior Gen AI role demands eval discipline.
A Pune contract-review startup runs 850 evals nightly across their RAG + agent pipeline. Each prompt change ships only if it raises aggregate score without breaking any individual case. Quality regressions that used to surface in customer support now surface in CI.
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