AI Feature Spec
A PRD template for product features that incorporate AI/ML models. Covers model selection rationale, confidence thresholds, fallback behaviour, evaluation criteria, and responsible AI considerations. Free to copy, download, and use. No signup required.
# AI Feature Spec **Feature name:** [Name] **PM:** [Name] **Engineering lead:** [Name] **AI/ML lead:** [Name] **Date:** [Date] **Status:** [ ] Draft [ ] In review [ ] Approved --- ## 1. Problem & motivation **What problem does this feature solve?** [Clear problem statement grounded in user evidence] **Why does this require AI?** [Explicit justification — could this be solved with rules or search? If not, why not?] **User evidence (source → frequency):** - [Source 1] — [N] mentions - [Source 2] — [N] mentions --- ## 2. Feature description **What the user experiences:** [Plain-language description of the feature from the user's perspective — no model jargon] **Input:** [What data goes into the model — user text, structured data, documents, images] **Output:** [What the model produces — label, score, generated text, ranked list] **Where in the product:** [Which screen / workflow / API endpoint] --- ## 3. Model & approach | Decision | Choice | Rationale | |---|---|---| | Model / approach | [e.g. Claude claude-sonnet-4-6, fine-tuned BERT, heuristic baseline] | | | Hosted vs self-hosted | [API / cloud / on-device] | | | Latency budget | [e.g. < 2s P95] | | | Cost per inference | [estimated] | | | Prompt vs fine-tune | [zero-shot / few-shot / fine-tuned] | | **Prompt design (if LLM):** ``` [Draft prompt or system instructions — include output format specification] ``` --- ## 4. Confidence & thresholds | Threshold | Behaviour | |---|---| | High confidence (≥ [X]) | Show result to user directly | | Medium confidence ([Y]–[X]) | Show result with caveat / "review suggested" | | Low confidence (< [Y]) | Fall back to human review / hide AI output | **How is confidence measured?** [Softmax probability / LLM self-reported confidence / separate classifier / heuristic] **Known cases where confidence is unreliable:** [e.g. "Model overconfident on short inputs", "Confidence not calibrated for domain X"] --- ## 5. Fallback behaviour **What happens when the model fails or is unavailable?** | Failure mode | Fallback | User message | |---|---|---| | Model API timeout / error | [Retry 1x, then show manual option] | "AI processing unavailable — you can continue manually." | | Low confidence output | [Show output with disclaimer] | "This suggestion may need review." | | Input outside expected distribution | [Reject or flag] | "We couldn't process this input. Try rephrasing." | | Rate limit exceeded | [Queue / degrade gracefully] | "High demand — your result will be ready in [X] seconds." | **Can the user correct or override the AI output?** [ ] Yes — describe edit/override UX: ________________________________ [ ] No — justify: ________________________________ --- ## 6. Evaluation criteria **What does "good enough" mean for this feature?** Before shipping, the model must meet these criteria on the evaluation dataset: | Metric | Target | Minimum acceptable | |---|---|---| | [Precision / Accuracy / BLEU / etc.] | [X%] | [Y%] | | [Latency P50] | [Xms] | [Yms] | | [Latency P95] | [Xms] | [Yms] | | [Cost per 1K requests] | [$X] | [$Y] | **Evaluation dataset:** - Size: [N examples] - Source: [how was it collected / labelled] - Known biases or gaps: [document them] **Human baseline (if applicable):** [What accuracy does a human expert achieve on this task? The model target should be set in relation to this.] --- ## 7. Online monitoring | Signal | Alert threshold | Owner | |---|---|---| | Model error rate | > [X%] | [Name] | | Latency P95 | > [Xms] | [Name] | | User override / correction rate | > [X%] | [Name] | | Thumbs down / negative feedback rate | > [X%] | [Name] | | Cost per day | > $[X] | [Name] | **How are we collecting feedback on AI output quality?** [ ] Explicit feedback (thumbs up/down) [ ] Implicit signal (user edits output, user ignores output) [ ] Manual sampling review [ ] None planned --- ## 8. Responsible AI | Consideration | Assessment | Mitigation | |---|---|---| | **Bias** — does the model perform worse for any user group? | | | | **Hallucination** — can the model generate false information confidently? | | | | **Privacy** — is user data sent to a third-party model provider? | | | | **Explainability** — can we explain why the model produced this output? | | | | **Over-reliance** — will users trust AI output without critical review? | | | | **Misuse** — can the feature be manipulated to produce harmful outputs? | | | **Data handling:** - Is user data used to train / fine-tune the model? [ ] Yes [ ] No - Is user data sent to a third-party API? [ ] Yes (provider: ________) [ ] No - Is data retained by the model provider? [ ] Yes [ ] No [ ] Unknown --- ## 9. Engineering tasks 1. [ ] Implement prompt / model inference call with timeout and retry logic 2. [ ] Add confidence threshold logic and fallback paths 3. [ ] Build user feedback collection (thumbs up/down or implicit signal) 4. [ ] Add logging: input hash, model version, confidence score, latency, cost per call 5. [ ] Set up monitoring alerts for error rate, latency, cost 6. [ ] Evaluation harness: run model against eval dataset, output metrics report 7. [ ] Feature flag to roll out to [X%] of users first 8. [ ] Rate limiting / cost cap to prevent runaway inference spend --- ## 10. Launch criteria - [ ] Evaluation metrics meet targets on held-out test set - [ ] Fallback behaviour tested and works correctly - [ ] Monitoring alerts configured and tested - [ ] Feature flag set to [X%] rollout - [ ] Privacy / legal review complete (if user data is sent to third-party) - [ ] Responsible AI checklist signed off
How to use this AI Feature Spec template
Write the fallback before writing the happy path
AI features fail in ways that standard software doesn't: model timeouts, low-confidence outputs, distribution shifts, hallucinations. If you don't design the fallback before engineering starts, engineers will improvise it under deadline pressure — usually by showing a confusing error message or silently degrading. Define fallback behaviour in the spec.
Set evaluation criteria before you see the model results
If you define 'good enough' after seeing the model's performance, you'll unconsciously anchor on what the model can achieve rather than what users need. Define the minimum acceptable metrics before the model is evaluated. If the model doesn't hit the minimum, the feature doesn't ship — or the approach changes.
Justify AI explicitly — default to simpler approaches
The spec asks: 'Why does this require AI?' This question catches features where a keyword filter, a sort algorithm, or a simple classifier would be more reliable and cheaper. AI is the right choice when the feature genuinely needs generalisation or generation. It's the wrong choice when it's the trendy option.
Treat user correction rate as your primary quality signal
After launch, the single most useful signal for AI output quality is how often users edit or override the output. A high correction rate means the model is saving users effort but not getting it right — useful. A very low correction rate could mean the model is great, or that users trust it without checking — dangerous. Instrument both.
Want a AI Feature Spec grounded in your actual customer data?
PMRead ingests your customer interviews, feedback, and Slack threads — and generates PRDs backed by real evidence, not guesses.
Frequently asked questions
Do I need this template for every feature that uses an LLM API call?
Yes, if the LLM output is user-facing. A background data-enrichment call that users never see can use a lighter-weight spec. But if users see, act on, or trust the model output, you need explicit decisions on confidence thresholds, fallback behaviour, and responsible AI — regardless of how simple the call seems.
What if we don't know which model to use yet?
Document the evaluation criteria first (Section 6) and then run model comparisons against those criteria. The model choice should be driven by your defined metrics — latency, quality, cost — not by what the team is most familiar with.
How do we handle the responsible AI section if we're a small team?
Fill it in honestly, even if the mitigations are lightweight. 'We reviewed and decided the bias risk is low because the feature only processes structured data' is a legitimate entry. The goal is explicit decision-making — not a compliance theatre checkbox. Small teams can do this in 20 minutes.
What's the difference between confidence threshold and evaluation accuracy?
Evaluation accuracy is measured offline against a labelled dataset before you ship. Confidence threshold is the real-time signal the model produces for each individual inference. You need both: offline accuracy tells you the model works on average; confidence thresholds determine how you handle cases where the model is uncertain in production.
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