GenAI Evaluation Harness
Systematic quality, safety and regression testing for LLM-powered systems.
A composable evaluation framework purpose-built for generative-AI applications. It provides repeatable test suites that measure factual accuracy, hallucination rate, toxicity, latency and cost across prompt versions and model upgrades. Integrated into CI/CD, it turns subjective model quality into quantifiable, gate-able metrics.
Key Features
Multi-Dimensional Scoring
Evaluates LLM outputs across accuracy, relevance, groundedness, toxicity and format compliance using configurable rubrics and LLM-as-judge techniques.
Regression Test Suites
Golden-dataset-driven test packs that detect output drift when prompts, models or retrieval pipelines change, with automatic pass/fail gating in the deployment pipeline.
Cost and Latency Profiling
Per-evaluation capture of token consumption, inference latency and estimated spend, enabling teams to balance quality against operational cost before promoting changes.
Human-in-the-Loop Annotation UI
A lightweight review interface for subject-matter experts to label edge cases, override automated scores and continuously improve the evaluation corpus.
Use Cases
Validating a RAG-based regulatory Q&A assistant
BankingEstablished automated evaluation gates that caught 92 percent of hallucinated regulatory references before they reached end users, reducing manual review effort by 60 percent.
Model migration for a customer-service summarisation engine
TelecommunicationsEnabled a zero-regression migration from GPT-3.5 to GPT-4o by running 1,200 golden-dataset evaluations across five quality dimensions and gating the release on score parity.
Continuous safety monitoring for a wealth-advice copilot
Wealth ManagementDeployed nightly evaluation runs that track toxicity, bias and suitability scores, feeding results into the firm's model-risk governance framework.
Technical Stack
Deliverables
- →Evaluation framework with scoring rubrics(Python package and configuration)
- →Golden-dataset templates and seed data(Versioned dataset repository)
- →CI/CD quality-gate integration(Pipeline configuration)
- →Evaluation dashboard and alerting rules(Grafana dashboard and alert definitions)
Expected Programme Outcomes
4–8 weeks
saved on evaluation framework build
55–70%
faster model-quality regression detection
50–65%
fewer undetected model regressions
3–5 months
of eval framework rework avoided
70–80%
faster model promotion decisions
Prerequisites
- →At least one GenAI application or prototype in development
- →Access to the LLM API used by the target application
- →A representative dataset or set of example queries and expected outputs
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