The problem

Most AI systems are judged by demo. Someone asks a handful of questions, the answers look impressive, and the system ships. Then real users arrive with queries no one tried, and the failures show up in production: confident wrong answers, retrieval that misses the relevant document, prompt injections that redirect the system, latency and cost that make the feature uneconomic, and quality that drifts as models and data change.

The underlying issue is that a fluent answer is not a correct one, and a system that looks good on ten questions can be wrong on a meaningful share of the next thousand. Without measurement there is no way to know which situation you are in. Teams cannot answer basic questions: how often is it right, where does it fail, is it safe to expose, and has the last change made it better or worse.

This is the gap most generic AI work leaves open. Building a prototype is now easy; knowing whether that prototype is fit to rely on is not, and it is where I do my most differentiated work.

What I design and build

I build the evaluation and reliability layer that tells you whether an AI system actually works and keeps working. This is deliberately model- and vendor-neutral: I assess the system you have, whoever built it.

The foundation is a golden set — a curated collection of representative inputs with known correct answers and, for retrieval systems, known correct sources — built from your real use cases rather than convenient examples. Against it I run layered evaluation: retrieval metrics such as recall@k and precision to test whether the right material is being found; RAGAS metrics and LLM-as-judge scoring for answer faithfulness and relevance; and human review on sampled outputs to keep the automated judges honest. I run failure analysis to characterise where and why the system breaks, not just a single score, and I probe for prompt-injection and jailbreak weaknesses with adversarial inputs. Regression testing then locks in what works, so every prompt tweak, model swap, or data update is measured against the same bar. Alongside correctness I measure latency and cost per query, because a system that is accurate but too slow or too expensive is not shippable.

Where it fits

This fits any LLM or RAG system heading toward, or already in, production: knowledge assistants, agentic workflows, document pipelines, and customer-facing features where being wrong has a cost. It is most valuable before a launch decision, after a model or prompt change, and as ongoing monitoring once live.

It is not worth a heavy evaluation harness for a throwaway internal experiment or a low-stakes tool where an occasional wrong answer costs nothing — I will tell you when measurement would cost more than the risk it retires. And evaluation reports on a system; it does not fix one. It tells you where and how badly something fails and points at the cause, but acting on that is a separate step, which I can also do or hand to your team.

How it works

I begin by building the golden set from your real cases. Inputs run through the system, and outputs are scored on several axes at once: retrieval quality where retrieval is involved, answer faithfulness and relevance through RAGAS and LLM-as-judge, and robustness through adversarial and prompt-injection inputs. Failure analysis groups the errors so the dominant failure modes are visible rather than buried in an average. That produces a baseline report. The golden set and its checks then become regression tests wired into the delivery pipeline, and once live, monitoring watches for quality drift, latency creep, and cost changes over time.

Security and privacy

Evaluation runs against sensitive data, so it respects the same boundaries as the system under test. Where required it runs on-premises or in your environment with no external API calls, using open-weight judge models when data cannot leave. Golden sets and evaluation results are stored under your control with role-based access, since they can contain sensitive inputs and reveal system weaknesses. Prompt-injection testing is conducted in a controlled setting, and the findings are handled as security-sensitive. Access to results and the reasoning behind each score are logged so conclusions are auditable rather than asserted.

Evaluation and reliability

Evaluation is the subject here, so the standard I hold applies to my own methods. Automated judges are themselves validated against human labels on a sample, so an LLM-as-judge score is trusted only where it has been shown to agree with human judgement, and I report that agreement rather than assuming it. Metrics are reported with enough of the distribution to be meaningful — not a single averaged number that hides a long tail of failures. Where a number is illustrative I frame it as a method, for example recall@k measured before and after a change, not as a promised result. The aim is a report you can act on and defend, with its limitations stated.

Deployment

Evaluation is delivered as a repeatable harness, not a one-off document. It is packaged so it runs on demand and in your CI pipeline, gating changes against the golden set. Monitoring integrates with your observability stack to track quality, latency, and cost on live traffic, and to flag drift as inputs and models shift. The golden set is maintained as a living asset that grows as new failure cases surface, so the system’s test coverage improves rather than decays.

How an engagement starts

This is the lowest-risk, most affordable way to start working with me. A production-readiness assessment is a short, fixed-scope engagement: I build an initial golden set from your real cases, run the layered evaluation, and deliver a baseline report on accuracy, failure modes, robustness, and latency and cost — with clear recommendations. You get an independent read on whether your AI system is fit to rely on, and a foundation you keep whether or not we work together further.

For evaluation applied to a multi-step agentic system, see the agentic workflow case study; for retrieval and answer evaluation on demanding bilingual legal text, see the Saudi Labor Law RAG case study.

FAQ

Can you evaluate a system you did not build?

Yes, and that is much of this work. The evaluation is model- and vendor-neutral. I assess the system you have against a golden set of your real cases, regardless of who built it or which models it uses.

What do I actually get from the assessment?

A baseline report: how often the system is right, where and why it fails, how it holds up against prompt injection and adversarial input, and its latency and cost per query — with prioritised recommendations. You also keep the golden set and evaluation harness as reusable assets.

How is LLM-as-judge trustworthy if a model is grading a model?

It is trusted only where validated. I check the automated judge against human labels on a sample and report the level of agreement. Where the judge does not align well with human judgement, I rely on human review instead rather than trusting the score.

Can this run against sensitive data without exposing it?

Yes. The evaluation can run on-premises or in your environment with open-weight judge models and no external calls, and golden sets and results stay under your control with role-based access.