LLM Evaluation Readiness Assessment: Can You Prove Your AI Works?
This free LLM evaluation readiness assessment scores your organization across ten dimensions of AI quality practice, and it is built for AI engineering leads, QA managers, and IT directors responsible for putting language models into production. It covers golden datasets, expected-answer authorship, scoring methods, CI gating, regression detection, retrieval evaluation, groundedness measurement, adversarial testing, production feedback loops, and documented acceptance criteria. Enterprise AI projects rarely fail on model capability. They fail because nobody could prove the system worked well enough to trust, so the business quietly stopped using it.
1. Do you have a golden dataset of real questions with verified correct answers?
A golden set is the foundation of every other evaluation practice. Without it, all quality claims are anecdotal.
2. Who authored the expected answers in your evaluation set?
3. How do you measure answer quality?
Exact string matching fails for generative output; most mature teams combine automated scoring with periodic human review.
4. Do evaluations run automatically before a prompt or model change ships?
5. Can you detect regressions when a model version or provider changes underneath you?
Hosted model endpoints are updated by the vendor without notice, and behavior can shift meaningfully.
6. How do you evaluate the retrieval layer separately from the model?
Most RAG failures are retrieval failures. Measuring only end answers hides where the system actually broke.
7. Do you measure groundedness and hallucination rate explicitly?
8. Do you test adversarial inputs such as prompt injection and out-of-scope questions?
Enterprise assistants get asked about salaries, competitors, and how to bypass their own guardrails within days of launch.
9. Is there a working feedback loop from production back into the evaluation set?
10. Do you have documented acceptance criteria that the business owner has signed off?
Without an agreed quality bar, launch decisions become opinion contests between engineering and the business.
How the assessment is scored
Ten questions are each scored from zero to three, giving a maximum of thirty points that is converted to a percentage. The questions are weighted equally on purpose: evaluation maturity is a chain, and a team with an excellent golden dataset but no CI gate will still ship regressions. Bands are set at 0-24%, 25-49%, 50-74%, and 75-100%. Answer for the practice you actually follow week to week, not the one documented in a slide deck. Most enterprise teams running their first production AI workload score between 25% and 45%, which is a normal starting point rather than a failure.
What separates mature evaluation practice
The teams that sustain enterprise AI in production share a small set of habits. None of them are expensive, but all of them require someone to own the work rather than treating quality as a shared afterthought that everyone agrees is important and nobody schedules. What separates these teams is not tooling sophistication, since most of this can be built with a spreadsheet of questions and a script. It is the discipline of running the check every time, including on the Friday afternoon change that seemed obviously safe. The habits below are the ones we look for first when auditing an AI program.
- Subject matter experts, not engineers or the model itself, author the expected answers in the golden set.
- Retrieval quality is measured separately from answer quality, so failures are localized to the right layer.
- Evaluation runs automatically in CI and blocks deployment when scores fall below an agreed threshold.
- Production failures are triaged into the golden set within days, so the test set grows with real-world usage.
Interpreting your band
A low score is not a verdict on your engineering team, it is a description of process maturity that can change in weeks. Focus on the lowest-scoring questions first, because evaluation capability compounds: a golden set enables automated scoring, which enables CI gating, which enables safe iteration speed. Pay particular attention to any question you scored zero on. Zero on golden datasets means every other quality claim in your program is unsupported. Zero on regression detection means a silent vendor-side model update can degrade your system for weeks before anyone notices. Fix zeros before optimizing anything already scoring two.
How Netray builds evaluation into AI delivery
Netray treats evaluation as a first-class deliverable on every AI engagement for manufacturers running Infor SyteLine, LN, M3, and ServiceMax. Before writing prompts we work with your subject matter experts to build a golden set from real questions your planners, quality engineers, and service technicians actually ask, with verified answers traced to source records. We stand up automated scoring, retrieval metrics, and groundedness tracking in your CI pipeline, all of which runs inside your network for ITAR and CMMC-constrained customers. The result is an AI system whose quality you can demonstrate to an auditor and to a skeptical plant manager.
Frequently Asked Questions
How large does a golden evaluation set need to be?
Start at fifty well-chosen cases and grow toward two hundred. Fifty is enough to catch obvious regressions and to force the useful argument about what a correct answer even looks like. Two hundred gives statistical stability so a two-point score change means something. Beyond a few hundred, additional coverage matters more than raw volume: add adversarial inputs, edge cases, and real production failures rather than more variations of questions you already handle well.
Is using an LLM as a judge reliable enough for enterprise use?
Yes, provided you calibrate it. Give the judge an explicit rubric, then score a sample of fifty cases with both the judge and a human expert and measure agreement. Above roughly eighty percent agreement the judge is a dependable regression detector. Below that, tighten the rubric or simplify the criteria. Never treat an uncalibrated judge score as ground truth, and always keep a periodic human review sample in the loop.
Why evaluate retrieval separately from the final answer?
Because most RAG failures are retrieval failures, and end-to-end scores cannot tell you that. If the correct passage never reached the model, no amount of prompt engineering will fix the answer, yet teams routinely spend weeks tuning prompts against a broken retriever. Measuring recall at k against labeled relevant passages separates the two problems immediately and usually shows that chunking, embedding choice, or reranking is where the real work belongs.
Get a concrete evaluation roadmap with a golden dataset plan, scoring design, and CI gating tailored to your AI use cases.
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