← Back to BlogLLM Evals Explained: Score Quality Before Users Feel the Bugs
How production teams measure LLM quality: offline golden sets, , agent success rates, loops, and the eval architecture Compile Vision builds before and after launch.
Evals turn “feels better” into a scoreboard you can ship against.
Listen to this briefing
Speech is not supported in this browser. Try Chrome or Edge.
Shipping an LLM feature without is flying without instruments. Demos look sharp until a prompt change, a model upgrade, or a new document corpus quietly tanks answer quality.
are the habit that separates prototypes from products: fixed test cases, scored dimensions (correctness, , tone, tool success), , and production feedback that feeds the next suite.
Use this briefing to stand up a practical eval loop for copilots, , and chat features, the same loop Compile Vision wires into client builds.
Offline: prove it before release
A is a versioned list of inputs with expected behaviors: citations present, SQL valid, refusal on unsafe asks, multi-hop answers that need specific documents.
Run the suite on every model or prompt change. Fail the build when or task success drops below your bar. catch boring regressions before users do; humans still spot-check hard cases.
Online: learn from real traffic
Thumbs, edits, escalations to humans, and tool-failure rates tell you what your missed. Sample production traces, redact, and promote painful failures into new offline cases.
Pair this with (latency, token cost, retrieval hits) so quality and unit economics move together. block harm; prove usefulness. closes the loop when users surprise you.
What to score (by product type)
), citation coverage, and answer correctness on labeled questions. adds multi-hop path checks. Strong is how you fight fluent .
: task completion, tool-call validity, step budget, and recovery after tool errors. Chat UX: tone, refusal quality, and PII leakage probes.
Prefer a small set of metrics you trust over a dashboard nobody reads. Start with 30 to 100 labeled cases per critical workflow, then grow from production misses into a living .
Judge models vs humans
scales cheaply for pairwise preference and rubric scores, but it inherits bias and needs calibration against human labels on a holdout set.
Compile Vision uses hybrid judging: automatic checks for structure and , LLM judges for graded rubrics, and periodic human review for legal, medical, or brand-sensitive flows.
How Compile Vision helps
We build eval harnesses next to your or agent stack: dataset versioning, , , and operator UIs that show why a case failed.
If you already run , are the complementary layer. reduce blast radius. tell you whether the product is still good.
Key takeaways
No means every prompt change is a gamble.
catch regressions; finds new failure modes.
Score what your product promises: , tools, tone, safety.
LLM judges need human calibration on a holdout set.
Compile Vision ships eval harnesses beside your LLM features.
Eval patterns teams actually run
Pick the lightest pattern that covers your riskiest workflows, then deepen.
| Approach | How it works | Best for | Differs by |
|---|---|---|---|
| Golden Q&A suite | Fixed questions with reference answers or must-include facts; score exact/semantic match and citations. | Support copilots and internal knowledge bots. | Simple to start; weak alone for open-ended writing. |
| RAG faithfulness pack | Score whether claims are supported by retrieved chunks; flag unsupported sentences. | Any grounded answer product. | Catches fluent hallucination even when retrieval is “fine”. |
| Agent trajectory eval | Grade tool sequences and final state against a task rubric, not just final text. | Workflow agents with CRM, email, or code tools. | Needs sandbox tools and clear success criteria. |
| Online feedback + promotion | Collect thumbs/edits, cluster failures, promote into offline cases each sprint. | Products with steady traffic after launch. | Keeps suites from rotting as users change behavior. |
Tools & stack options
Practical pieces teams use to prototype and ship.
| Tool | Role |
|---|---|
| Ragas | RAG-oriented metrics (faithfulness, relevancy) |
| Promptfoo | Prompt/model regression testing in CI |
| LangSmith / Langfuse / Phoenix | Tracing, datasets, and annotation |
| DeepEval | Python assert-style LLM tests |
| OpenAI Evals / custom harnesses | Structured eval runners |
| Braintrust | Eval experiments and scorecards |
| Human annotation UIs | Calibrate judges and label goldens |
| Compile Vision eval console | Suite versioning, CI gates, failure review |
What you can build
- CI that blocks deploys when RAG faithfulness drops
- Per-tenant eval slices for multi-customer SaaS
- Agent sandboxes that score tool success before production
- Nightly model bake-offs with the same golden set
- Promotion pipelines from production thumbs to new tests
- Shared scorecards across RAG, CAG, and GraphRAG variants
Architecture & diagrams
Offline + online eval loop
Release gates and live learning reinforce each other.
What each product type should measure
Match metrics to the failure mode you care about.
Essential reading
References & further reading
Sources matter as much as the briefing itself. Start with the primary reference, then dig into the supporting links.
Primary reference for RAG faithfulness and related metrics.
- 2Compile Vision: RAG explained for builders
Retrieval patterns your eval suite should stress-test.
- 3Compile Vision: AI guardrails for production
Safety rails complement quality evals; both belong in production.
- 4Compile Vision: AI agents explained
Agent loops need trajectory evals, not only final-answer checks.
- 5Promptfoo
Open-source prompt and model regression testing.
Learning / build steps
- 1
Map risks
Workflows where bad answers hurt most
- 2
Build goldens
Versioned cases with expected behavior
- 3
Automate scores
Structure, faithfulness, task success
- 4
Gate releases
Fail CI when metrics regress
- 5
Close the loop
Promote production failures into the suite
Need evals before your next model swap?
Compile Vision builds golden suites, CI gates, and production feedback loops for RAG and agents so quality stops being a gut feel. Share your workflows and we will propose a scoreboard you can ship against.
Contact Compile VisionRead next
Continue with related Compile Vision research briefings and builder guides.
14 min read
RAG Explained for Builders: Ground AI Answers in Your Data
A detailed briefing on RAG types compared: vector RAG, hybrid, GraphRAG, Agentic RAG, vectorless, multimodal, and more, plus tools and how Compile Vision ships production document QA.
11 min read
Building Production RAG Pipelines for Enterprise Search
A production briefing on enterprise RAG: service boundaries, security, hybrid retrieval, tooling, failure modes, and how Compile Vision ships document QA your teams can trust.
14 min read
AI Agents Explained: From Chatbots to Autonomous Workflows
A newspaper-style briefing on AI agents in 2026: agent types compared (ReAct, tool-calling, multi-agent, swarm, voice, coding), tools, build paths, and how Compile Vision can ship one for you.