AI Technical Specification Template: Bridge the Gap Between PM and Engineering
TL;DR
An AI feature spec isn't a standard PRD. It needs to specify model requirements, data needs, performance thresholds, fallback behavior, and evaluation criteria that a standard product requirements document never covers. This template closes the translation gap between what you want the AI to do and what engineering needs to actually build it.
Problem and Success Definition
Before any technical specification, align on what problem the AI is solving and how you'll know if it's solved. Ambiguity here creates re-work later.
Problem statement
One paragraph describing the user pain and the specific task the AI will perform. Avoid 'the AI will understand user intent' — say 'the AI will classify support tickets into 8 predefined categories with ≥90% accuracy.'
User persona and context
Who uses this feature, in what workflow, with what level of AI trust. A first-time user encountering AI extraction for the first time requires different UX treatment than a power user relying on it daily.
Primary success metric
One measurable outcome that defines success for this feature. Example: 'Support ticket routing accuracy ≥90% within 30 days of launch.' Not 'users will find it helpful.'
Secondary success metrics
2–3 supporting metrics that provide diagnostic signal. Example: false positive rate by category, user override rate, time saved per ticket.
Non-goals (explicit)
What the AI explicitly will NOT do in v1. This prevents scope creep and helps engineering stay focused. Example: 'The model will not generate response drafts — classification only.'
Model Requirements
Task type
Classification, extraction, generation, ranking, summarization, or a combination. Each task type has different model selection criteria, evaluation approaches, and failure modes.
Input specification
Exactly what the model receives: text length range, language(s), format (plain text, HTML, structured fields). Include min/max token estimates. Engineering can't build a pipeline without knowing the input shape.
Output specification
Exactly what the model must return: JSON schema, field names, value constraints, confidence scores. Provide 3–5 concrete input/output examples. These become your evaluation test cases.
Performance thresholds
Minimum acceptable accuracy, precision, recall, or task-specific metric. Specify by user segment or input category if performance varies. Example: 'Accuracy ≥90% for English tickets; ≥80% for Spanish tickets.'
Latency and throughput SLA
Maximum acceptable p95 response time for the user-facing feature. Maximum batch processing latency for async operations. These drive model selection — a 200ms requirement rules out many larger models.
Data Requirements
Training and fine-tuning data
Source, volume, format, and labeling requirements. If fine-tuning is planned, specify minimum dataset size (typically 500–1000 examples per class for classification). If using a foundation model via prompting, specify the few-shot examples.
Evaluation dataset
A held-out set of labeled examples used to measure model performance. Must be representative of production distribution. Should include edge cases and known failure modes. Minimum 200 examples for reliable evaluation.
Data privacy and governance
Can training data include PII? What anonymization is required? Who owns the data? Which data can be sent to external API providers? These constraints must be resolved before engineering starts, not after.
Data pipeline requirements
What real-time or batch data does the model need at inference time? What's the freshness requirement? What happens if the data source is unavailable? These define the infrastructure complexity.
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Integration, Fallback, and Safety
System context diagram
Where does this AI component sit in the product architecture? What calls it, what does it call? A simple box diagram clarifies integration dependencies that prose descriptions obscure.
API contracts
Request/response schema for the AI service. This is the interface contract between PM requirements and engineering implementation. Version it — AI API contracts change when prompts or models change.
Fallback behavior
What happens when the AI returns low confidence, an error, or an invalid response? Options: show nothing, show a default, route to human review, show a degraded version. Specify the threshold that triggers each fallback.
Content safety and guardrails
What inputs should be rejected before reaching the model? What outputs must be filtered? For user-facing AI, always specify: profanity filtering, PII detection, and output length limits as minimum safety layers.
Testing and Acceptance Criteria
Performance acceptance gate
The model must achieve [metric] ≥ [threshold] on the held-out evaluation dataset before integration testing begins. If this is not met, engineering and PM review the spec for scope reduction or the model selection for alternatives.
Integration test cases
5–10 end-to-end test scenarios covering: happy path, edge cases, boundary conditions, and known failure modes. Each test case specifies: input, expected output, and acceptable variation.
Human evaluation protocol
For subjective output quality (summaries, responses, recommendations), define who evaluates, what rubric they use, what sample size is sufficient, and what score passes. 'Looks good to the team' is not a protocol.
Monitoring requirements at launch
What will you monitor from day one? Minimum: model error rate, latency p95, cost per request, and the primary success metric. Who owns the dashboard? Who gets alerted and at what threshold?
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Technical specification, engineering collaboration, and AI delivery are core curriculum in the AI PM Masterclass. Taught by a Salesforce Sr. Director PM.