LLM Evaluation (Deep Workflow)

Evaluation turns “it feels better” into reproducible evidence. Design around failure modes your product cares about—not only aggregate scores.

When to Offer This Workflow

Trigger conditions:

• - Prompt or model change; need before/after proof
• Building CI for LLM outputs; flaky quality in production
• RAG/agents: grounding, tool use, safety regressions

Initial offer:

Use six stages: (1) define quality & constraints, (2) build datasets & rubrics, (3) automatic metrics, (4) human evaluation, (5) regression & gates, (6) online validation & iteration. Confirm latency/cost budgets and risk (PII, safety).

Stage 1: Define Quality & Constraints

Goal: Name dimensions that map to user harm if they fail.

Typical dimensions (pick what matters)

• - Correctness / task success; groundedness (RAG); faithfulness to sources
• Safety: policy violations, jailbreaks, PII leakage
• Style: tone, brevity, format (when product-critical)
• Robustness: paraphrase, multilingual, edge inputs

Constraints

• - Max tokens, latency p95, cost per request; locale requirements

Exit condition: Weighted priority of dimensions; non-goals stated.

Stage 2: Datasets & Rubrics

Goal: Fixed eval sets + clear scoring rules.

Practices

• - Stratify by intent: easy/medium/hard; adversarial slice separate
• Rubrics: 1–5 scales with anchors; binary checks for safety
• Version datasets (git or table); no silent edits without changelog
• Privacy: synthetic or redacted real examples per policy

Exit condition: Golden set size justified; inter-rater plan if human scoring.

Stage 3: Automatic Metrics

Goal: Fast signals—know limitations.

Options

• - Reference-based: BLEU/ROUGE—often weak for assistants
• Model-as-judge: fast, biased—calibrate vs human
• Task-specific: exact match, JSON schema validity, tool-call args match
• RAG: citation overlap, nugget recall, entailment models (use carefully)

Hygiene

• - No training on test; detect leakage from prompts

Exit condition: Each auto metric has known blind spots documented.

Stage 4: Human Evaluation

Goal: Authoritative judgment where automatic metrics lie.

Design

• - Sample size for confidence; blind A/B when possible
• Guidelines + examples; adjudication for disagreements
• Locale-native raters when language quality matters

Exit condition: Human scores correlate enough with auto for ongoing monitoring—or you rely on human for release.

Stage 5: Regression & Gates

Goal: Block bad deploys in CI or release pipeline.

Gates

• - Must-pass suites: safety, critical user journeys
• Trend tracking: not only point-in-time
• Canary with online metrics (see Stage 6)

Artifacts

• - Report: model/prompt id, dataset versions, scores, diff

Exit condition: Rollback criteria defined before rollout.

Stage 6: Online Validation

Goal: Production truth—shadow, A/B, or gradual ramp.

Signals

• - Implicit: thumbs, edits, task completion, support tickets
• Explicit: user ratings (sparse)

Causality

• - Confounds: seasonality, cohort—control where possible

Final Review Checklist

• - [ ] Quality dimensions prioritized for the product
• [ ] Versioned eval sets and rubrics
• [ ] Auto + human roles explicit; limitations documented
• [ ] Release gates and rollback tied to metrics
• [ ] Plan for online feedback loop

Tips for Effective Guidance

• - Slice metrics—averages hide regressions on critical intents.
• For agents, evaluate trajectories, not only final text.
• Never claim objective truth—evaluation is operationalized judgment.

Handling Deviations

• - No labels: start with smallest pairwise comparison set + spot human review.
• High-stakes (medical/legal): human-in-the-loop gate; disclaim limits of auto eval.