The AI Feature Technical Handoff Template: What PMs Must Document Before Engineering Starts

Why AI Features Need a Different Handoff Document

Traditional software features have deterministic outputs. Given the same input, the feature does the same thing. Testing is straightforward: write test cases, confirm expected output, ship. A standard PRD works because the PM can specify behavior precisely.

AI features are different in three structural ways that a standard PRD does not handle well.

Probabilistic outputs

The model produces different outputs each call. There is no single expected output to test against. Quality is measured as a distribution (how often is the output good enough?), not as a pass/fail on individual cases. The handoff document must specify what good enough means, as a measurable threshold, not as a subjective description.

Hidden inputs: the model itself

The model version is part of the feature's specification. If engineering builds against Claude Sonnet 5 and the team upgrades to Sonnet 5.5 three months later, behavior may change. The handoff must specify which model, which version, and what the process is for evaluating model upgrades before they reach production.

Data contracts are part of the feature

What data gets sent to the model, in what format, and under what access controls is a product decision with legal, compliance, and cost implications. A handoff that says 'we will pass the user's message to the API' is incomplete. The handoff must document exactly what goes in the prompt, what stays out, and why.

Failure looks different

A traditional feature either works or crashes. An AI feature can fail silently by producing plausible-looking bad output. A document summarizer that confidently makes up a key finding looks like it is working. The handoff must define what failure looks like, how to detect it, and what happens when it is detected.

Section 1: Model Requirements

This section locks in the model specification so engineering builds against a defined target, not a moving one.

Model name and version

Specify exactly. Not 'Claude Sonnet' but 'claude-sonnet-5-5' or the specific version string used in the API. If the team has not run evals yet, note the date by which model selection must be finalized and who owns that decision.

Example: claude-sonnet-5-5. Model selection finalized by [DATE]. Owner: [ML engineer name].

Primary task type

Classification, extraction, generation, summarization, code generation, reasoning, retrieval-augmented generation, or agentic. This drives which evaluation approach is appropriate and which failure modes to expect.

Example: Structured extraction (JSON). The model extracts structured data fields from unstructured customer support tickets.

Required response format

Exact output schema. If the model must return JSON, specify the schema. If it must return a list, specify the format. If it must respond in a specific tone or length range, document those constraints. Vague output requirements are the most common source of rework in AI feature development.

Example: JSON with fields {category: string, urgency: low|medium|high, summary: string (max 200 chars)}. Use structured outputs mode to enforce schema.

Latency and throughput requirements

What is the maximum acceptable p95 latency for this feature? What is the expected requests-per-second at peak? Streaming or synchronous? These requirements determine model choice, infrastructure size, and whether provisioned throughput is needed.

Example: p95 latency under 3 seconds for the end user. Peak load: 50 requests per minute. Streaming preferred for UI responsiveness.

Section 2: Data and Prompt Contract

The prompt contract is the specification for what goes into the model call. This section prevents accidental data leakage, compliance violations, and prompt injection vulnerabilities before they are built in.

Inputs allowed in the prompt

List every data field that will be included in the prompt. For each field: the data type, the source (user input, database, third-party API), and whether it needs sanitization before inclusion. User-supplied text always needs sanitization to prevent prompt injection. PII in prompts requires explicit legal sign-off in most jurisdictions.

Inputs explicitly excluded

Document what you are deliberately NOT sending to the model. Passwords, payment card numbers, health records, internal system identifiers. Make the exclusion list explicit. The most common data exposure in AI features is accidental inclusion of fields that should have been excluded.

System prompt ownership

Who writes and maintains the system prompt? The PM owns the intent; the ML engineer owns the implementation. Document the current version, where it lives in the codebase, who can approve changes, and what the review process is for production system prompt edits. System prompt changes are effectively feature changes and deserve the same review as code changes.

Data retention and logging

Are prompts and completions logged? For how long? Can they be used for model improvement? This must be specified before launch, not discovered during a compliance review post-launch. Default to logging for debugging, with PII scrubbing before any log analysis, unless your privacy policy says otherwise.

Write AI Specs That Engineering Can Build From

The AI PM Masterclass covers AI feature specification, from model requirements and data contracts to acceptance criteria and rollback planning.

Section 3: Failure Modes and Guardrails

This is the section most PMs skip because it requires thinking about what goes wrong before it has gone wrong. It is also the section engineering most needs, because guardrail design depends on knowing what specific failure modes to defend against.

Output quality failure

The model produces a plausible-looking but wrong output. For each primary use case, define the worst-case wrong output and the business consequence. For a contract summarizer, a wrong summary could cause a legal team to miss an obligation. The guardrail might be a confidence score threshold below which the feature falls back to a human review queue.

Instruction following failure

The model ignores format instructions and returns free text when JSON was required. Defense: use structured outputs mode or function calling if the provider supports it. Document the fallback behavior when the parser fails: return an error to the caller, retry once, or fall back to a default value.

Out of scope input

A user sends input the model was not designed to handle (a different language, a completely unrelated topic, an attempt to extract system prompt contents). Document the expected behavior for out-of-scope inputs: graceful refusal message, redirect to a human, or silent drop.

Harmful output

The model produces output that violates your content policy or causes direct harm to users. For each content category your product touches (legal advice, medical information, financial guidance), document the guardrail: input filter, output classifier, or human-in-the-loop review. Make explicit whether a harmful output should return an error or a generic fallback message.

Model provider outage

Your inference provider has an incident and the model is unavailable. What is the fallback behavior? Silent degradation (hide the feature), user-visible error message, or a cached or deterministic fallback? Document the fallback, the timeout threshold, and the circuit breaker logic if relevant.

Section 4: Acceptance Criteria and Eval Requirements

This section defines what done means for an AI feature. Without it, QA has no standard to test against and the feature ships on vibes.

Minimum quality threshold

Express quality as a number. The feature ships when [quality metric] is above [threshold] on [eval dataset]. Example: the summarizer achieves a factual accuracy rate above 92 percent on a 50-document test set drawn from the production distribution. This number is negotiated between PM and ML engineer before the sprint starts, not discovered at demo time.

Eval dataset specification

Where does the eval set come from? Who curates it? How big is it? A good eval set for a production AI feature has at least 50 examples that represent the real distribution of inputs the feature will see, including edge cases and adversarial inputs. The PM is responsible for defining the distribution; the ML engineer is responsible for building the eval harness.

Regression criteria for model upgrades

If the team upgrades the model (e.g., from claude-sonnet-5 to claude-sonnet-5-5), what quality regression is acceptable before the upgrade is blocked? Define this before you are in a production incident trying to decide whether a 3 percent regression is acceptable. Common practice: accept up to 2 percent regression on secondary metrics if the primary metric improves by more than 5 percent.

Human evaluation protocol

Some AI feature quality cannot be measured automatically. For subjective quality dimensions (tone, creativity, relevance), document how many human evaluations are required, who the evaluators are, and what the rubric is. A 30-sample human eval with two reviewers and an interrater agreement above 80 percent is a concrete, testable acceptance criterion.

Section 5: Launch Configuration and Rollback Plan

The last section of the handoff covers how the feature goes live and what happens if it needs to come back down. These decisions are much harder to make under incident pressure than before launch.

Rollout strategy

What percentage of traffic sees the AI feature at launch? Most teams start at 10 to 20 percent and ramp over one to two weeks. Document the rollout percentage at each stage, the metric threshold that must hold to proceed, and who has the authority to approve the ramp.

Feature flag configuration

Which feature flag controls this feature? What is the flag name, the owner, and the emergency kill-switch procedure? Every AI feature should be behind a flag that can be turned off in under 60 seconds by an engineer who is not on call. Document the exact steps.

Monitoring and alerting

What metrics does the team watch in the 48 hours after launch? Output error rate, latency p95, inference cost per user, user engagement with the feature output, and explicit negative feedback signals. Set alert thresholds before launch, not during the incident.

Rollback criteria

Document exactly when to roll back: if quality drops below [threshold] on the production eval, if output error rate exceeds [percentage], if inference cost exceeds [budget] per day, or if a harmful output incident occurs. A rollback criteria document that is written before launch gets followed; one written after the fact does not.

How to use this template

Copy the section headers into your team's PRD template or a standalone doc. Fill in each field in a 60-minute working session with the ML engineer before the sprint starts. The doc is not done until every field has an owner and a definition. Blank fields are not acceptable; if a field does not apply, write "Not applicable: [reason]." The handoff doc lives next to the PRD in your project management tool and is updated when any of the specified values change in production.