How We Evaluate LLM Applications Before They Ship

Every LLM application we build goes through a structured evaluation process before it reaches production. This isn't about running a few test prompts and eyeballing the results — it's a systematic framework that gives us confidence in quality, catches regressions early, and provides ongoing monitoring after deployment. Here's how we approach it.

The three pillars of LLM evaluation

We evaluate LLM applications across three dimensions: functional correctness (does it produce the right output?), behavioral alignment (does it behave according to specifications?), and operational fitness (does it perform within acceptable latency, cost, and reliability bounds?). Each dimension requires different evaluation methods and metrics.

Building golden datasets

The foundation of any evaluation framework is a high-quality dataset of input-output pairs. We build golden datasets collaboratively with domain experts, starting with 50-100 representative examples that cover the full range of expected inputs. Each example includes the input, the expected output, and annotations explaining why that output is correct. We categorize examples by difficulty, topic, and edge case type so we can analyze performance across dimensions.

Golden datasets are living documents. We add new examples whenever we discover failure modes in production, and we retire examples that no longer represent realistic usage. We version the dataset alongside the application code so evaluation results are always reproducible.

Automated evaluation with LLM-as-judge

For subjective quality dimensions — coherence, helpfulness, tone — we use LLM-as-judge evaluation. A separate LLM scores the application's outputs against structured rubrics. The key to making this reliable is specificity: instead of asking 'is this response good?', we ask 'does this response answer the user's specific question?', 'does it cite sources from the provided context?', 'does it avoid making claims not supported by the context?'. Each criterion gets a binary or 1-5 score with required justification.

We calibrate our LLM judges against human evaluations on a subset of examples. If the judge's scores diverge significantly from human scores, we refine the rubrics. This calibration step is essential — an uncalibrated LLM judge can give you false confidence.

Continuous monitoring in production

Evaluation doesn't stop at deployment. We monitor production quality using a combination of automated checks (response length, format compliance, latency), sampled LLM-as-judge evaluation (scoring a percentage of production responses), and user feedback signals (thumbs up/down, follow-up questions, session abandonment). We set up alerts for quality degradation and have runbooks for common failure patterns.

The evaluation pipeline in practice

Our evaluation pipeline runs automatically on every code change. It takes about 15 minutes for a full evaluation pass across the golden dataset. Results are displayed in a dashboard showing scores by category, trend lines over time, and flagged regressions. If any metric drops below threshold, the deployment is blocked until the team reviews and either fixes the issue or updates the threshold with justification.