# LLM Evaluation Readiness Assessment: Can You Prove Your AI Works? > Source: https://netray.co/tools/llm-evaluation-readiness-assessment > Publisher: Netray (netray.co), AI and integration engineering for ERP and manufacturing systems. > Section: Free Tools ## Short answer Score your organization across ten dimensions of LLM evaluation maturity, from golden datasets and regression gates to human review and production monitoring. ## Category on-prem-ai ## Tool **Kind:** assessment **Questions:** #### Do you have a golden dataset of real questions with verified correct answers? **Help:** A golden set is the foundation of every other evaluation practice. Without it, all quality claims are anecdotal. **Options:** ##### Yes, 200+ curated cases covering normal, edge, and adversarial inputs **Score:** 3 ##### Yes, roughly 50-200 cases reviewed by subject matter experts **Score:** 2 ##### An informal list of a few dozen test questions **Score:** 1 ##### No structured test set; we spot-check by hand **Score:** 0 #### Who authored the expected answers in your evaluation set? **Options:** ##### Subject matter experts, with a second reviewer signing off **Score:** 3 ##### Subject matter experts, single reviewer **Score:** 2 ##### The engineering team, using their best judgment **Score:** 1 ##### The model itself generated the expected answers **Score:** 0 #### How do you measure answer quality? **Help:** Exact string matching fails for generative output; most mature teams combine automated scoring with periodic human review. **Options:** ##### Defined rubric scored by an LLM judge, calibrated against human ratings **Score:** 3 ##### Automated scoring with a rubric, not yet calibrated to humans **Score:** 2 ##### Manual review of a sample after each change **Score:** 1 ##### We rely on user complaints to surface quality problems **Score:** 0 #### Do evaluations run automatically before a prompt or model change ships? **Options:** ##### Yes, in CI with a hard pass threshold that blocks the deploy **Score:** 3 ##### Yes, automated but advisory rather than blocking **Score:** 2 ##### Manually triggered when someone remembers **Score:** 1 ##### No pre-deployment evaluation exists **Score:** 0 #### Can you detect regressions when a model version or provider changes underneath you? **Help:** Hosted model endpoints are updated by the vendor without notice, and behavior can shift meaningfully. **Options:** ##### Yes, scheduled evaluations run continuously and alert on score drops **Score:** 3 ##### We re-run evaluations whenever we are notified of a version change **Score:** 2 ##### We would probably notice through user feedback **Score:** 1 ##### No regression detection at all **Score:** 0 #### How do you evaluate the retrieval layer separately from the model? **Help:** Most RAG failures are retrieval failures. Measuring only end answers hides where the system actually broke. **Options:** ##### We track recall and precision at k against labeled relevant passages **Score:** 3 ##### We check whether the correct source appeared in the retrieved set **Score:** 2 ##### We inspect retrieved chunks informally when an answer looks wrong **Score:** 1 ##### We only evaluate final answers **Score:** 0 #### Do you measure groundedness and hallucination rate explicitly? **Options:** ##### Yes, every answer is scored for citation support with a tracked hallucination rate **Score:** 3 ##### Yes, on a sampled basis with a documented rate **Score:** 2 ##### We require citations but do not verify them systematically **Score:** 1 ##### No groundedness measurement **Score:** 0 #### Do you test adversarial inputs such as prompt injection and out-of-scope questions? **Help:** Enterprise assistants get asked about salaries, competitors, and how to bypass their own guardrails within days of launch. **Options:** ##### Yes, a maintained adversarial suite runs on every release **Score:** 3 ##### We red-teamed once before launch **Score:** 2 ##### Ad hoc testing by whoever thinks of it **Score:** 1 ##### No adversarial testing **Score:** 0 #### Is there a working feedback loop from production back into the evaluation set? **Options:** ##### Yes, flagged production failures are triaged and added to the golden set weekly **Score:** 3 ##### Yes, reviewed periodically and added in batches **Score:** 2 ##### Feedback is collected but rarely turned into test cases **Score:** 1 ##### No production feedback capture **Score:** 0 #### Do you have documented acceptance criteria that the business owner has signed off? **Help:** Without an agreed quality bar, launch decisions become opinion contests between engineering and the business. **Options:** ##### Yes, quantified thresholds per use case, approved in writing **Score:** 3 ##### General targets agreed verbally with the business owner **Score:** 2 ##### Engineering has internal targets the business has not seen **Score:** 1 ##### No defined acceptance criteria **Score:** 0 **Bands:** #### No evaluation discipline: you are flying blind **Min:** 0 **Max:** 24 **Verdict:** You currently cannot tell whether a change improves or damages your AI system, which means every deployment is a gamble. Teams in this band typically discover quality problems through user complaints weeks after they were introduced. This is the single highest-risk posture for an enterprise AI program and it usually ends in a quiet cancellation. **Recommendations:** - Build a 50-question golden set this month with subject matter experts writing the expected answers. - Stop shipping prompt changes without running that set first, even if the run is manual. - Instrument a simple thumbs-up and thumbs-down control in the interface and route every negative to a triage queue. - Define a written acceptance threshold with the business owner before the next release. - Pick one use case and get it fully evaluated rather than partially covering five. #### Emerging practice: real gaps remain **Min:** 25 **Max:** 49 **Verdict:** You have started evaluating but the practice is manual, inconsistent, or too narrow to catch the failures that matter. Most teams here measure end answers while ignoring retrieval quality and groundedness, so they know something is wrong without knowing which layer broke. This is workable for a pilot and dangerous at scale. **Recommendations:** - Separate retrieval evaluation from answer evaluation so you can localize failures to the right layer. - Automate your evaluation run in CI, starting as advisory before making it blocking. - Add explicit groundedness scoring and start tracking hallucination rate as a headline metric. - Expand the golden set to 150-200 cases including edge and out-of-scope questions. - Assign a single named owner for evaluation quality rather than treating it as everyone's side task. #### Solid foundation: tighten the loop **Min:** 50 **Max:** 74 **Verdict:** You have a real evaluation practice with automated runs and a maintained dataset. The remaining gaps are usually adversarial coverage, judge calibration, and the speed of the production feedback loop. Closing them turns evaluation from a release checkpoint into a continuous quality signal. **Recommendations:** - Calibrate your LLM judge against human ratings on a sample and measure the agreement rate. - Build a maintained adversarial suite covering prompt injection and out-of-scope requests, and run it every release. - Make the evaluation gate blocking in CI with an agreed pass threshold. - Shorten the production feedback cycle so flagged failures reach the golden set within a week. - Add continuous scheduled evaluation so vendor-side model changes are detected automatically. #### Mature evaluation program **Min:** 75 **Max:** 100 **Verdict:** Your evaluation practice is strong enough to support production AI in a regulated environment. You can defend quality claims with evidence and ship changes with confidence. The work now is scaling the discipline to new use cases without diluting it, and keeping the golden set representative as the business evolves. **Recommendations:** - Template your evaluation harness so each new use case inherits the practice on day one. - Review golden set representativeness quarterly against the actual distribution of production queries. - Track evaluation cost and runtime so the gate does not become a bottleneck as coverage grows. - Publish quality metrics to business stakeholders on a regular cadence to sustain trust and funding. - Extend groundedness and adversarial testing to any agentic or tool-calling workflows you add. ## Content **Intro:** 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. **Sections:** #### How the assessment is scored **Body:** 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 **Body:** 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. **Bullets:** - 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 **Body:** 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 **Body:** 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. **Cta:** Get a concrete evaluation roadmap with a golden dataset plan, scoring design, and CI gating tailored to your AI use cases. **Faq:** #### How large does a golden evaluation set need to be? **Answer:** 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? **Answer:** 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? **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. ## Search terms LLM evaluation readiness, AI model evaluation framework, LLM regression testing, golden dataset LLM evaluation, enterprise AI quality assurance, LLM as judge evaluation, AI hallucination testing process --- Talk to an engineer: https://netray.co/contact?intent=erp_ai | Machine catalog: https://netray.co/agent/catalog.json