LLM Evaluation & QA (Evals-as-Code)

Evaluation Harnesses for Prompts, RAG Pipelines, and Agent Workflows

Shipping LLM features without structured evaluation is shipping without tests. We build evaluation harnesses that run prompts, RAG pipelines, and agent workflows against defined scenarios and score the results on accuracy, relevance, completeness, and format compliance.

These harnesses are code, not notebooks. They live in your repository, run in CI, and produce deterministic, comparable results across runs. Engineers treat them the same way they treat unit and integration tests: they write them, review them, and trust them to catch regressions.

Fry Express designs harnesses to be modular. Adding a new evaluation dimension or swapping a scoring method does not require rewriting the framework.

Golden Datasets, Synthetic Test Generation, and Scenario Coverage

Evaluations are only as good as the data they run against. We deliver golden datasets curated from real usage patterns and edge cases, supplemented by synthetic test generation that covers scenarios your production data has not yet surfaced.

Scenario coverage mapping ensures that critical paths are tested explicitly: high-value queries, adversarial inputs, multi-turn conversations, and cases where the model should decline to answer. Gaps in coverage are visible and tracked, not hidden.

The datasets are versioned alongside the evaluation harnesses. When a new failure mode is discovered in production, it becomes a test case that prevents recurrence. Over time, the dataset grows into a reliable quality baseline for your specific domain.

CI Regression Gates for Quality, Latency, and Cost

A prompt change that improves accuracy but doubles token consumption is not necessarily an improvement. We integrate regression gates into your CI pipeline that block merges when quality scores drop, latency exceeds thresholds, or cost per request increases beyond acceptable bounds.

These gates run automatically on every pull request that touches prompts, model configurations, or retrieval logic. Results are posted as PR comments with clear pass/fail indicators and links to detailed evaluation reports.

The thresholds are configurable per feature and per environment. A development branch may tolerate wider margins than a production release candidate. Fry Express helps you define the right thresholds based on your business requirements and tighten them as the system matures.

Safety Evaluation for Policy Compliance and Adversarial Resistance

Quality evaluation alone does not cover safety. We deliver safety-specific test suites that probe for policy violations, jailbreak susceptibility, and data leakage. These tests run alongside functional evaluations so that safety is assessed on every change, not only during periodic audits.

The test suites cover your organisation's content policies, regulatory constraints, and data handling rules. They include adversarial prompts designed to bypass guardrails, multi-step attack patterns, and scenarios where the model might inadvertently expose sensitive information from its context.

Results feed into the same CI gates as quality metrics. A prompt change that passes quality checks but fails a safety evaluation does not ship.

Release Scorecards With Acceptance Criteria and Improvement Loops

Before a release reaches production, stakeholders need a clear, concise answer to one question: is this version better than the last one? We produce release scorecards that summarise evaluation results against measurable acceptance criteria agreed upon before development began.

Scorecards cover quality, safety, latency, cost, and any domain-specific metrics relevant to the feature. They are generated automatically from CI evaluation runs and require no manual assembly. A release either meets its criteria or it does not.

Beyond individual releases, the scorecards feed into continuous improvement loops. Trends across releases reveal whether quality is improving, where regressions recur, and which areas of the system need deeper investment. This turns evaluation from a gate into a strategic tool.

These deliverables establish a discipline where LLM quality and safety are measured, enforced, and improved with every release. Regressions are caught before they reach users, and every deployment decision is backed by data rather than intuition.