Make AI outputs measurable and controllable - define acceptance criteria, build evaluation datasets, and implement regression testing so prompt and model changes don’t introduce risk.
AI systems are probabilistic: small changes to prompts, tools, retrieval settings, or model configuration can materially change outputs. Without evaluation and regression testing, teams end up shipping changes based on anecdotal examples, leading to quality drift, increased risk, and inconsistent user trust. OpenAI documents evaluation approaches and tooling for assessing model and agent behaviour, and Microsoft’s AI platform guidance also emphasises responsible operation and measurement when deploying AI solutions.
LW IT Solutions implements prompt evaluation and testing as an operational capability. We define what ‘good’ looks like for your use case, build a repeatable evaluation harness (golden question sets, expected outcomes, scoring), and integrate testing into your delivery workflow. This provides objective evidence for change approvals and enables structured improvement over time - reducing the risk of regressions while increasing answer quality and consistency.
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Service Overview
Highlights
- Golden dataset design based on real user questions and edge cases
- Clear pass/fail thresholds rather than anecdotal testing
- Support for prompt, retrieval, tool, and model configuration changes
- Evidence-driven governance aligned to approval and release controls
- Repeatable testing that scales as use cases grow
Business Benefits
- Reduced risk of quality regressions when prompts, models, or tools change
- Objective evidence to support change approval and release decisions
- Improved consistency and reliability of AI outputs for users
- Clear visibility of failure modes and edge cases before they reach production
- Faster, safer iteration through repeatable and automated evaluation
Typical use cases
- Customer support or service desk AI assistants
- Internal knowledge search and question-answering tools
- Agent workflows that rely on tool calling or retrieval
- Regulated or high-risk AI use cases requiring audit evidence
- Teams iterating rapidly on prompts and models without losing control
Objectives & deliverables
What Success Looks Like
- Define what acceptable AI output looks like in measurable terms
- Detect regressions early when prompts or configurations change
- Provide objective evidence for AI change approval decisions
- Improve output quality through structured iteration and testing
- Embed evaluation as a standard part of AI operations
What You Get
- Evaluation pack: acceptance criteria, scoring rubric, thresholds, and test datasets
- Evaluation harness: scripts/workflows and reporting outputs for repeatable runs
- Regression test plan: required tests for prompt, tool, retrieval, and model changes
- Governance alignment: evidence expectations for approval gates and release decisions
- Backlog: prioritised improvement areas based on observed failure modes
How It Works
- Discovery - confirm the AI use case, failure risks, and what ‘good’ looks like for users and stakeholders.
- Design - define evaluation dimensions, scoring, and dataset approach; agree thresholds and governance alignment.
- Build - implement the evaluation harness and reporting outputs; create initial golden sets and edge cases.
- Baseline - run an initial evaluation to establish current performance and identify high-impact improvements.
- Operationalise - integrate evaluation into change workflow; define cadence and owners.
- Improve - iterate using evidence: tune prompts, retrieval settings, or tools, then re-evaluate before release.
Engagement Options
- Foundation - establish acceptance criteria, golden sets, and an initial evaluation baseline
- Integration - embed evaluation and regression testing into existing CI/CD or release workflows
- Quality Uplift - analyse failures and improve prompt quality against defined metrics
- Ongoing Assurance - periodic re-evaluation and reporting as models or use cases evolve
Common Bundles
Customers who use this service often bundle with these services
RAG / Chat with Your Data
Build governed RAG chat with your data solutions using secure retrieval, permissions-aware context, and measurable answer quality controls.
AI Safety, Governance & Risk
Implement practical AI safety and governance with policies, approvals, logging, data boundaries, and controls that reduce operational and compliance risk.
OpenAI Agents (AgentKit) & Agents SDK Builds
Build production-grade OpenAI agent workflows using AgentKit and the Agents SDK, with tool integration, tracing, evaluation, and controlled operations.
Prompt Libraries & Templates
Governed prompt libraries and templates delivering role based standards, versioning and handover so teams use AI consistently safely.
Frequently Asked Questions
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