LLM Evaluation & AI Reliability Engineering

It is the discipline of making AI systems measurable, testable, and monitorable, the way site reliability engineering did for infrastructure. In practice it means an evaluation suite built from real questions, graders that judge every answer, a CI gate that blocks regressions, guardrails that constrain behaviour, and production monitoring that catches drift. The output is evidence: you know how good your AI is, and you know it before your users do.

Yes, and this is one of the most common ways engagements start. We do not need the vendor’s cooperation or their source code: we need access to the system, your real questions, and your documents to verify answers against. You get a numbers-backed verdict on correctness, grounding, refusal behaviour, and latency, plus a prioritised list of what to fix, whoever fixes it.

An independent assessment of an existing system typically runs 3 to 6 lakh rupees over about two weeks. Building the full reliability layer, golden set, graders, CI gate, guardrails, and monitoring, usually lands between 8 and 20 lakh rupees depending on how many workflows it covers. Ongoing retainers for monitoring and suite maintenance are scoped monthly. Against one production incident in front of a client, the suite tends to pay for itself quickly.

Four to six weeks from the first conversation to a CI gate that can block a release. The assessment takes about two weeks, the golden set and graders another two to three, and wiring the gate one more. A useful first version often runs earlier, because thirty verified cases already catch the regressions that matter most.

A version-controlled golden set, graders and judge prompts treated as code, a CI job that gates releases, guardrail configurations, tracing and dashboards, an incident playbook, and documentation your engineers can run without us. Nothing is locked to our tooling: the suite is plain data and ordinary code in your repositories.

Only after calibration, which is why we never skip it. We run the judge over cases humans have already graded and measure agreement before trusting it, use a different model family for judging than for answering, and spot-check a sample of verdicts every month. Where a deterministic check or a fact rubric can do the job, we use that instead: the judge is the escalation, not the default.

The DPDP Act’s rules for Significant Data Fiduciaries include due diligence that algorithmic systems do not put data principals’ rights at risk. An evaluation history is exactly the evidence that duty asks for: documented tests, pass rates over time, guardrail logs, and incident records. We structure the reporting so your compliance team can hand it over as is.

Not if the loop runs. Every answer a user flags is triaged weekly, encoded as a new golden case, and re-run on every release from then on, so the suite grows where the product actually hurts. We also schedule runs against provider model updates, which arrive silently and change behaviour. You can run this loop yourself after handover or keep us on a retainer to run it with you.

Traditional QA verifies deterministic behaviour: the same input produces the same output, and a test either passes or fails. LLM systems are probabilistic, so reliability work judges answers against expected facts and tolerances, tracks quality as rates rather than booleans, and treats the model itself as a dependency that changes underneath you. The discipline is the same, the mechanics are new, and most QA teams pick up the loop quickly once the suite exists.

Yes, if you want us to. Evaluation findings usually point at retrieval, prompting, guardrails, or model choice, and we build and run all of those layers. Some clients take the report and fix things internally, some hand us the top of the list. The evaluation is honest either way: the suite judges our fixes by the same numbers it judges everything else.