LLM Evaluation Harness & Guardrails

About this Gig

You stop guessing whether your LLM feature is good and start measuring it. I build an evaluation harness for your RAG or LLM app that scores accuracy, relevance, faithfulness, and safety on a dataset built from your real use cases, using RAGAS, DeepEval, and custom checks. I add guardrails for the failure modes that actually hurt you, hallucination, prompt injection, leaking sensitive data, off-topic answers, and wire tracing with self-hosted Langfuse so every response is logged and inspectable. You get a repeatable test suite that runs in CI, a quality baseline with numbers, and a regression gate so a prompt or model change cannot quietly make things worse. This is the same evaluation discipline I built into a production RAG for a large South African bank and my agentic SaaS work. This is a scoped, milestone-based engagement that starts with a short discovery call, with the final timeline confirmed after scoping. For an LLM evaluation harness that is typically around 3 to 4 weeks.

Requirements

Access to the LLM or RAG feature you want evaluated, a sample of real inputs and the answers you consider correct, the failure modes that worry you most, and where the harness should run (CI, your cloud). If you have no labeled data yet, I can help build a starter set. A short scoping call helps.

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