Short answer: an LLM evaluation template is a one-page plan with eight parts: the feature and the decision the eval supports, success criteria written as numbers, a failure taxonomy from real traces, a golden dataset of 20 to 100 examples, a grader for each criterion (code, LLM judge or human), a judge validation step, thresholds and a release gate, and a cadence for rerunning it. The full fill-in template is below, ready to copy.
AllthingsPM is an AI PM course and PM interview prep platform. Its course, built from 604 real PM job postings, has a full chapter on evals that teaches every part of this template, from reading traces to the CI gate, and ends in a graded case study where you build the suite for your own feature.
Evals are not a niche skill any more. In the job posting corpus behind the AllthingsPM course, 124 of the 389 PM postings in the working file mention evals or evaluation. If you are a PM on an AI feature, someone will ask you for this plan.
What is in the AI eval plan template?
Copy this block into a doc or a spreadsheet. Fill it from the top down, and do not skip section 3: the failure taxonomy is what makes every later section honest.
AI EVAL PLAN TEMPLATE (AllthingsPM)
0. FEATURE AND DECISION
Feature / surface: ______________________________
Who uses it, for what job: ______________________
Decision this eval supports:
[ ] ship or not [ ] pick a model [ ] pick a prompt [ ] catch regressions
Owner (one quality lead who has the final say): __________
1. SUCCESS CRITERIA (specific, measurable, achievable, relevant)
C1 Task success: ______ % of cases pass on ______________
C2 Grounding / faithfulness: ______ % of claims supported by context
C3 Safety / policy: ______ % of outputs pass the policy check
C4 Tone and format: ______ % match the style spec
C5 Latency: p95 under ______ ms C6 Cost: under $______ per request
2. TRACE REVIEW (before any tooling)
Traces read so far: ______ (read at least 30; aim for about 100)
Where they came from: [ ] production logs [ ] dogfood [ ] synthetic
Open notes file: ____________________
3. FAILURE TAXONOMY (named from what you saw, ranked)
F1 __________________ frequency ____ % severity [ ] low [ ] high
F2 __________________ frequency ____ % severity [ ] low [ ] high
F3 __________________ frequency ____ % severity [ ] low [ ] high
F4 __________________ frequency ____ % severity [ ] low [ ] high
4. GOLDEN DATASET
Size to start: ______ (20 to 50 for a first cut; grow toward 100+)
Slices: happy path ____ edge cases ____ adversarial ____
multilingual ____ past incidents ____
Each row: input | context | expected behaviour | failure mode it tests
Labelled by: ______________ Refresh cadence: ______________
5. GRADERS (cheapest one that can see the failure)
Criterion | Grader type | Output
C1 | [ ] code [ ] LLM judge [ ] human | pass / fail
C2 | [ ] code [ ] LLM judge [ ] human | pass / fail
C3 | [ ] code [ ] LLM judge [ ] human | pass / fail
C4 | [ ] code [ ] LLM judge [ ] human | pass / fail
Judge model: ______________ Judge prompt version: ______
6. JUDGE VALIDATION
Human-labelled set for the judge: ______ examples (train / dev / test)
Agreement with humans on test split: ______ % (target 75 to 90%)
True positive rate ______ True negative rate ______
Known biases checked: [ ] position [ ] verbosity [ ] self-preference
7. THRESHOLDS AND RELEASE GATE
Ship if: C1 >= ____ %, C2 >= ____ %, C3 = ____ %, no F-high regressions
Block if: ______________________________
Runs per case: ____ (agents: report pass^k, not only pass@k)
Where it runs: [ ] every prompt change [ ] CI [ ] nightly
8. ONLINE SIGNALS AND CADENCE
Production signals: thumbs-down rate ____ escalations ____ edits ____
Business outcome it should move: ______________
Every thumbs-down becomes: [ ] a new golden row [ ] a taxonomy update
Review meeting: ______________ Next plan review date: __________
The published guidance agrees on the part people get wrong: you do not need thousands of examples to start. Anthropic says 20 to 50 tasks drawn from real failures "is a great start" [1], and Hamel Husain and Shreya Shankar suggest reading at least 30 traces and working from a pool of about 100 [2]. The AllthingsPM course teaches the same size in its lesson on turning one complaint into thirty golden examples.
How do you fill in each section of the eval plan?
Section 0: name the decision first
An eval exists to make a decision easier. "Should we ship the new summariser prompt?" is a decision. "Measure quality" is not. Write the decision down, and name one quality owner. Husain and Shankar recommend a single domain expert as the final voice on quality, a "benevolent dictator", to avoid labelling fights and decision paralysis [2].
How AllthingsPM does this: the course's roadmap around an evaluable slice lesson teaches you to scope the feature so that a decision like this is possible before you write the PRD.
Section 1: write success criteria as numbers
Anthropic's guide asks for criteria that are specific, measurable, achievable and relevant, and lists common dimensions: task fidelity, consistency, relevance and coherence, tone and style, privacy preservation, context utilisation, latency and price [3]. Its worked example sets several targets at once, such as an F1 of at least 0.85 and 99.5% non-toxic outputs on a held-out test set [3]. Copy that shape: several numbers, each tied to one dimension.
Section 2: read traces before buying tools
Husain and Shankar call error analysis "the most important activity in evals" [2]. They report spending 60 to 80% of development time on error analysis and evaluation [2]. Read at least 30 traces yourself, write open notes on what went wrong, and keep going until new traces stop showing new failures [2].
How AllthingsPM does this: the first lesson in the evals chapter is literally read one hundred real traces before you buy any eval tooling.
Section 3: turn notes into a failure taxonomy
Group your notes into a short list of named failure modes, then rank them by frequency and severity. This list is your backlog. Every later section points back to it: each golden row tests a failure mode, and each grader checks one.
How AllthingsPM does this: the course lesson name the failure from one taxonomy, and rank the backlog walks through this ranking, and attribute every failure to a layer teaches you to say whether the model, the context, the harness or the surface is at fault.
Section 4: build the golden dataset
A golden dataset is a fixed set of inputs, with context and the expected behaviour, that you rerun on every change. Slice it: happy path, edge cases, adversarial inputs, other languages and past incidents. OpenAI's guidance warns against "vibe-based evals" and datasets that do not reflect production, and asks you to test beyond the happy path, including multilingual and conflicting prompts [4].
How AllthingsPM does this: two lessons cover this directly, turn one complaint into thirty golden examples and from raw data to a golden sample. If your product takes images or documents, the multimodal golden set lesson covers what text evals miss.
Section 5: pick the cheapest grader that can see the failure
Anthropic lists three grader types: code-based (fast, objective, reproducible), model-based (flexible, catches nuance) and human (the gold standard, used for calibration) [1]. Use code where a rule works: valid JSON, a required field, a banned phrase, a correct tool call. Use an LLM judge for tone, helpfulness and grounding. Use humans to calibrate the judge.
Grade pass or fail. Husain and Shankar say binary evaluations "force clearer thinking and more consistent labeling" than 1 to 5 scales [2], and Arize says binary outputs "tend to produce more stable and reliable evaluations" [5].
How AllthingsPM does this: pay for the cheapest evaluator that can see the failure and write the criterion: binary by default are the two course lessons behind this section.
Section 6: validate the judge
An LLM judge is a model, and models are wrong in patterned ways. Eugene Yan's review of the research lists position bias, verbosity bias (judges prefer longer answers) and self-enhancement bias (judges rate their own model higher) [6]. He also notes that pairwise comparison tends to be more stable than direct scoring for subjective judgments [6].
So label a set by hand, split it into train, dev and test, and measure how often the judge agrees with humans, including its true positive and true negative rates [2]. Arize suggests that 75 to 90% agreement with human labels means the judge is ready to scale, and warns: "If your judge is passing everything, your eval probably isn't hard enough" [5].
Section 7: set thresholds and a release gate
Write the gate before you see the results, or you will move it. For agents, run each case several times. Anthropic separates pass@k (at least one of k tries succeeds) from pass^k (all k tries succeed) and says the second matters for customer-facing agents that must behave the same way every time [1]. It also advises grading the outcome, not the path the agent took [1].
How AllthingsPM does this: eval-driven development teaches the failing eval before the fix and the CI gate, and evaluate an agent, not an answer covers final-state checks and pass^k reliability.
Section 8: connect to production and the business
OpenAI calls evaluation "a continuous process" and recommends evaluating early and often [4]. In practice, every thumbs-down, escalation and user edit is a candidate golden row. Then name the business outcome the eval should move, such as deflection or task success, so the number means something to your leadership.
How AllthingsPM does this: the course lesson on the thumbs-down that becomes an eval row covers the feedback loop, and business outcomes, not eval scores covers the link to adoption, deflection and ROI.
What does a finished eval plan look like?
Here is the template filled in for an illustrative feature: an AI assistant that drafts replies to support tickets from a help-centre knowledge base. The targets are examples to show the shape, not benchmarks.
| Section | Filled in |
|---|---|
| 0. Decision | Ship the new drafting prompt to all agents, or keep the old one. Owner: support quality lead |
| 1. Criteria | C1 draft accepted with light edits in 80% of cases; C2 every policy claim cites a help-centre article; C3 zero drafts promise refunds outside policy; C5 p95 under 4 seconds |
| 2. Traces | 100 recent tickets with the old prompt's drafts, read by the owner |
| 3. Taxonomy | F1 invents a policy (high); F2 answers the wrong question (high); F3 too long (low); F4 wrong language (low) |
| 4. Golden set | 40 rows: 15 happy path, 10 edge cases, 5 adversarial, 5 non-English, 5 past incidents |
| 5. Graders | Code: language match, length cap, citation present. LLM judge: grounded in cited article (pass/fail). Human: weekly sample of 20 |
| 6. Judge check | 60 hand-labelled rows split train/dev/test; judge agreement on test split recorded before rollout |
| 7. Gate | Ship if C1 and C2 targets met, zero F1 failures, no drop versus old prompt on any slice |
| 8. Online | Agent edits and rejections logged; each rejected draft reviewed for a new golden row; outcome tracked: tickets resolved without escalation |
Every row traces back to a failure someone actually saw.
What mistakes make an eval plan useless?
- Metrics before traces. Generic vendor metrics miss the failures your product actually has.
- Likert scales. A 3 versus a 4 means different things to different graders. Binary pass or fail per criterion is easier to label and to act on [2].
- Trusting an unvalidated judge. Without an agreement check against humans, a judge score is an opinion with a decimal point [5].
- One run per case for agents. An agent that passes once may fail the next time. Report pass^k where consistency matters [1].
- No link to outcomes. A rising score with flat outcomes measures the wrong thing.
How AllthingsPM does this: the chapter's graded integration case makes you build the suite for a feature you carry through the course, from your own trace backlog, so each of these mistakes shows up in your own work and gets graded.
How do you use this template in an AI PM interview?
Interviewers at AI companies ask eval questions as design problems: "Offline evals improved but users say the model got worse. What do you do?" The template gives you a structure to answer out loud: name the decision, say you would read traces, build a taxonomy, check whether the golden set reflects production, check the judge, and tie it to an outcome.
You can practise with real questions such as a customer saying an agent underperforms in German or a model that wins benchmarks but fails on long tasks. Each question page on AllthingsPM has an answer guide and starts a scored AI mock interview that asks follow-ups. For a specific role, paste the job description into the JD mock.
For background reading, see our AI evals guide for product managers, Lenny's Podcast evals episodes, summarised and evaluation awareness.
Why AllthingsPM is the better choice for learning AI eval plans
AllthingsPM puts all three in one account. The AI PM course was built from 604 real PM job postings and has 14 chapters, 101 lessons and 14 graded case studies; its evals chapter maps one lesson to each section of this template, from trace sampling to agent evals. The question bank holds 4,122 real questions from 260 companies, including eval design questions from AI labs, and each one starts a scored AI mock. The jobs catalog lists 116 live PM job descriptions at 18 AI companies, each with a mock built from it, so you can rehearse for an evals role like Abridge's.
The rivals have real strengths. Anthropic and OpenAI write the clearest docs on their own platforms, and Hamel Husain and Shreya Shankar's FAQ is the deepest free reading on error analysis. Use them as references. To learn the whole plan, practise it and prove it, AllthingsPM is the better choice: there is a free tier, and Pro is $20 a month or $120 a year.
Open the evals chapter of the AllthingsPM course and fill in the template on your own feature as you go.
Frequently asked questions
What is an LLM evaluation template?
It is a written plan for how you will judge an AI feature's outputs: the decision the eval supports, success criteria as numbers, a golden dataset, graders for each criterion, a check that any LLM judge agrees with humans, and a release gate. The eight-part template above is free to copy.
What is the best LLM evaluation template for product managers?
AllthingsPM's AI eval plan template, above, because it ties every section to a decision and a business outcome and each section has a matching lesson in the AllthingsPM course. Anthropic's success criteria guide and Arize's judge templates are good references for single sections.
How many examples do I need in a golden dataset?
Fewer than most people think to start. Anthropic says 20 to 50 tasks drawn from real failures is a great start [1], and Husain and Shankar suggest reading at least 30 traces and working from a pool of about 100 [2]. Grow the set as production failures come in.
Should I use an LLM as a judge?
Yes, for criteria code cannot check, such as tone or grounding, but validate it first. Label examples by hand, measure agreement on a held-out split, and watch for position, verbosity and self-preference biases [2][6]. Arize treats 75 to 90% agreement with humans as ready to scale [5].
Should evals use pass or fail, or a 1 to 5 score?
Pass or fail per criterion. Binary grades force clearer decisions, label more consistently and need fewer samples to detect a change than Likert scales [2][5].
Where can I learn to build eval plans as a PM?
The AllthingsPM AI PM course has a full evals chapter with lessons on traces, taxonomies, golden sets, graders, rubrics, eval-driven development and agent evals, plus a graded case study. Start free at AllthingsPM/course/evals.
Sources
- Anthropic, "Demystifying evals for AI agents": https://www.anthropic.com/engineering/demystifying-evals-for-ai-agents
- Hamel Husain and Shreya Shankar, "Frequently Asked Questions (And Answers) About AI Evals": https://hamel.dev/blog/posts/evals-faq/
- Anthropic, "Define your success criteria": https://platform.claude.com/docs/en/docs/test-and-evaluate/define-success
- OpenAI, "Evaluation best practices": https://developers.openai.com/api/docs/guides/evaluation-best-practices
- Arize, "LLM as a Judge: Primer and Pre-Built Evaluators": https://arize.com/guides/llm-as-a-judge/
- Eugene Yan, "Evaluating the Effectiveness of LLM-Evaluators (aka LLM-as-Judge)": https://eugeneyan.com/writing/llm-evaluators/
- AllthingsPM AI PM course, chapter 8, Evals: https://allthingspm.app/course/evals
- AllthingsPM course job posting corpus, September 2026: count of postings in the working file that mention evals or evaluation
