Quality engineering service
LLM Evaluation Services
Replace demo confidence with repeatable evidence for the models, prompts, retrieval pipelines, and application workflows your users actually depend on.
Service explained
What does an LLM evaluation service test?
An LLM evaluation service tests whether a language-model application meets defined quality, safety, reliability, latency, and cost requirements for its intended users. QA-CS begins with the real workflow rather than a generic leaderboard: the prompts, model configuration, retrieval sources, structured outputs, business rules, safeguards, and human review points that shape production behaviour. We convert representative tasks, known failures, edge cases, and policy constraints into versioned evaluation cases with explicit acceptance criteria. Objective outcomes use deterministic checks where possible; contextual qualities use calibrated rubrics and expert review. The same cases can compare prompts, models, retrieval changes, or guardrails under controlled conditions and can be rerun as the application changes. Results record the tested configuration, evidence, scoring basis, observed failures, uncertainty, and limitations. The purpose is a defensible release decision for a bounded use case, not a claim that one score proves universal model quality or safety.
Business value
What this service helps you achieve
Evaluate LLM applications for quality, groundedness, safety, reliability, latency, and cost with human-reviewed production evidence.
Compare prompts, models, retrieval, and safeguards under controlled conditions
Turn evaluation results into reviewable release criteria
When to use this service
Recognise the need before risk becomes delay.
We evaluate the LLM as part of the application it serves. Scope can include prompts, system instructions, retrieval, grounding, structured outputs, safeguards, latency, cost, and human escalation because production quality depends on the complete workflow, not a model score in isolation.
- An LLM feature is moving from prototype to production
- A prompt, model, RAG pipeline, or safeguard is changing
- Teams need release evidence beyond demos and generic benchmarks
Our approach
Evidence at every stage.
We evaluate the LLM as part of the application it serves. Scope can include prompts, system instructions, retrieval, grounding, structured outputs, safeguards, latency, cost, and human escalation because production quality depends on the complete workflow, not a model score in isolation.
- 01Define the application, users, decisions, and failure costs
- 02Build representative datasets and scoring criteria
- 03Run repeatable evaluations across agreed configurations
- 04Review failures, limitations, and release evidence
Evaluation scope
Measure the application, not an isolated model response.
Production behaviour is shaped by more than the selected model. System instructions, prompt templates, retrieval quality, context construction, output schemas, guardrails, orchestration, and user interaction can all change the result. We map those components to the user outcomes and business consequences they influence, then define coverage at the right layer.
- Model and prompt behaviour
- RAG retrieval, grounding, and citation quality
- Structured outputs and workflow integration
- Safety, privacy, latency, and cost constraints
Scoring design
Choose evidence that matches each requirement.
Exact checks are appropriate for schemas, calculations, required fields, citations, and other objective outcomes. Rubrics and human review are necessary when quality depends on relevance, completeness, tone, or domain judgement. Model-assisted graders may increase scale, but they are calibrated against labelled examples and inspected for bias, inconsistency, and disagreement.
- Deterministic assertions for objective outcomes
- Reference-based and reference-free metrics
- Calibrated rubrics for contextual quality
- Human adjudication for consequential or ambiguous results
Release assurance
Make evaluation repeatable across meaningful change.
A useful evaluation set is versioned alongside the application context it measures. When a model, prompt, knowledge source, safeguard, or orchestration step changes, teams can rerun relevant coverage, compare with a known baseline, inspect failure clusters, and document accepted exceptions. The result becomes a maintained release control rather than a one-time benchmark exercise.
- Versioned datasets and run configuration
- Baseline comparison and regression signals
- Failure analysis with reproducible evidence
- Documented thresholds, exceptions, and residual risk
Deliverables
Clear outputs your team can use
Documentation is concise, traceable, and written for engineering, product, and business stakeholders.
- Evaluation strategy and risk taxonomy
- Versioned datasets, rubrics, and acceptance criteria
- Comparative results with failure analysis
- Human-reviewed release report and maintained regression plan
Related services
Build a connected engagement
Follow the risks into the product, platform, people, or operational areas that influence the same outcome.
LLM Evaluation Framework
Build an LLM evaluation framework with representative datasets, suitable scorers, release thresholds, human review, and production feedback.
Read guideLLM Evaluation Metrics
Choose LLM evaluation metrics for correctness, groundedness, relevance, safety, latency, cost, and user outcomes without hiding risk in one score.
Read guideAI Agent Evaluation Services
Evaluate AI agents for task success, tool use, grounding, safety, reliability, latency, and cost with human-reviewed release evidence.
Explore serviceEvidence base
Methods and authoritative references
Technical claims are bounded to the cited material and the stated QA-CS methodology. Sources were reviewed on 15 August 2026.
- NIST AI RMF: Generative AI Profile
Primary risk-management guidance for evaluating generative AI systems.
- UK AI Security Institute: Inspect
Official description of an open-source evaluation framework built around datasets, solvers, tools, and scorers.
- Anthropic: Demystifying evals for AI agents
Primary engineering guidance on tasks, trials, graders, transcripts, outcomes, and evaluation harnesses.
Frequently asked questions
What teams usually ask
Reviewed by the QA-CS quality engineering teamContent updated . Scope and controls are confirmed for each engagement.
What kinds of LLM applications can be evaluated?
Scope can cover assistants, RAG applications, summarisation, classification, content generation, structured extraction, copilots, and other model-backed workflows. The evaluation design follows the user task and consequence of error.
Can QA-CS compare models, prompts, or RAG configurations?
Yes. Controlled comparisons can hold the task set and scoring criteria constant while relevant prompts, models, retrieval settings, or safeguards change.
Which LLM evaluation metrics should we use?
Metrics should follow the requirement. Exact checks suit objective outcomes; groundedness, relevance, completeness, and similar qualities usually need task-specific rubrics, calibrated automated scoring, and human review.
Does a passing evaluation guarantee production safety?
No. Results apply to the tested cases, configuration, and conditions. Reports state coverage, assumptions, uncertainty, limitations, and residual risk so accountable people can make an informed decision.
Start with clarity
Discuss llm evaluation services
Share the product, release, or operational challenge. We will help define the right next step.