AI implementation · Planning guide
Evaluate AI systems against real tasks, not impressive demos.
Production LLM evaluation needs repeatable datasets, task-specific criteria, human review, automated checks, regression testing, and monitoring. A model that produces convincing answers is not automatically producing correct or useful ones.
Who this is for
AI product teams and reviewers defining acceptance evidence for language-model applications.
What to leave with
A representative evaluation set, scoring rubric and release comparison process.
Workflow design
Reference approach: LLM evaluation
Use this sequence to identify interfaces, review points and evidence. Adapt the stages to your systems; it is a planning reference, not a client result.
- 01
Define expected behavior
Specify task success, prohibited outputs and the evidence a reviewer needs for each case.
Output
A task-specific evaluation rubric
- 02
Build representative cases
Combine normal requests, difficult examples, known failures and cases requiring refusal or escalation.
Output
A versioned evaluation dataset
- 03
Score and investigate
Use appropriate deterministic and human checks; calibrate model-based scores against reviewed examples.
Output
An error analysis tied to system components
- 04
Compare before release
Run the same evaluation across proposed changes and connect production findings to future test cases.
Output
A documented release decision and feedback loop
Controls across the workflow
- Named source and workflow owners
- Reviewable acceptance evidence
- Explicit access and operating boundaries
- Recorded exceptions and next actions
Decisions that shape the scope
- Does the dataset represent difficult cases?
- Include ambiguous requests, access failures, missing context and known regressions alongside normal traffic patterns.
- Can releases be compared consistently?
- Version examples and configuration, record results and agree which regressions prevent release.
LLM evaluation starts with defining what good actually means
There is no single universal score for a production language-model application. Evaluation criteria should reflect the task the system performs, the evidence available to it, the risks of a wrong answer, and the behaviour users expect. Reliable evaluation combines deterministic checks, model-based evaluation, human judgement, and production feedback.
Decisions to work through
01
Build representative test sets
Use real task patterns, edge cases, difficult queries, known failures, and high-risk scenarios rather than relying only on hand-picked examples.
02
Evaluate the full system
Separate model behaviour from retrieval, tool use, prompts, application logic, data quality, and other components that influence the final result.
03
Measure regressions
Re-run stable evaluation datasets whenever prompts, models, retrieval, tools, or application logic change.
04
Connect offline and production signals
Use production traces, user feedback, escalations, failures, and sampled reviews to improve the evaluation suite over time.
Review before you proceed
Use this checklist to structure the discussion. Ticking an item records your review here; it does not certify readiness. Your selections reset when you reload.
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Comparison
Core layers of an LLM evaluation framework
| Area | What to evaluate | Why it matters |
|---|---|---|
| Task success | Whether the system completes the business or user task it was designed to perform. | A technically fluent answer can still fail the actual workflow. |
| Correctness | Whether outputs are accurate according to trusted reference data, expected results, or expert review. | Language quality is not a substitute for factual or task correctness. |
| Groundedness | Whether claims are supported by retrieved evidence, supplied context, or approved data sources. | This helps detect unsupported model claims in RAG and knowledge applications. |
| Retrieval | Whether the system finds the correct evidence before generation. | Poor retrieval often looks like a model failure even when the model is behaving correctly. |
| Human review | Expert judgement for nuance, quality, usefulness, risk, tone, and difficult cases. | Some behaviours cannot be reliably reduced to automated scores. |
| Automated evaluation | Rule-based checks, classifiers, similarity methods, model-based judges, and task-specific evaluators. | Automation makes repeatable evaluation possible across larger test suites. |
| Regression testing | Comparison of current behaviour against an established baseline after system changes. | Model, prompt, data, and retrieval changes can improve one area while damaging another. |
| Production monitoring | Failures, latency, feedback, escalation, cost, policy events, and sampled output quality. | Offline testing cannot represent every situation encountered after deployment. |
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Frequently asked questions
LLM evaluation: questions and answers
01What is LLM evaluation?
What is LLM evaluation?
LLM evaluation is the process of testing language-model behaviour and the applications built around it against defined criteria such as correctness, task success, groundedness, safety, usefulness, and reliability.
02What metrics should be used to evaluate an LLM?
What metrics should be used to evaluate an LLM?
The right metrics depend on the task. Evaluation may include exact or deterministic checks, retrieval metrics, groundedness, classification scores, human ratings, model-based judges, task completion, safety checks, latency, and operational measures.
03What is LLM-as-a-judge?
What is LLM-as-a-judge?
LLM-as-a-judge uses a language model to assess outputs according to defined criteria. It can scale qualitative evaluation, but should itself be validated against human judgement and known examples.
04How do you evaluate a RAG system?
How do you evaluate a RAG system?
Evaluate retrieval and answer generation separately. Test whether the correct evidence is retrieved and then whether the generated response accurately uses that evidence.
05How often should LLM evaluation be run?
How often should LLM evaluation be run?
Regression evaluations should be run whenever important system components change, including models, prompts, retrieval logic, tools, data pipelines, guardrails, or application behaviour.
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