AI Integration Requirements Template
A technical requirements template for integrating AI capabilities into existing systems. Covers API specifications, data flows, authentication, error handling, and acceptance criteria to ensure smooth integration between AI services and your technology stack.
Overview
What's included
Integration Overview
Integration Overview
Project name: Author: Date: Version:
Integration Purpose
Describe what the integration achieves and why:
Systems Involved
| System | Role | Owner | Technology |
|---|---|---|---|
| AI service provider | |||
| Consumer / calling system | |||
| Data source | |||
| Downstream recipient |
Integration Pattern
- Synchronous API call — Request/response in real time
- Asynchronous (webhook) — Submit request, receive callback
- Batch processing — Process data in scheduled batches
- Event-driven — Trigger on events via message queue
- Streaming — Continuous data flow (e.g. SSE, WebSocket)
Data Flow Diagram
[Source System] ---(request)---> [API Gateway] ---(transform)---> [AI Service]
↑ |
└────────────────────(response)────────────────────────────────┘
Describe the data flow in detail:
API Specification
API Specification
Endpoint: _______________
Method: GET / POST / PUT / DELETE URL: Content type: application/json Authentication: API key / OAuth 2.0 / JWT /
Request Schema
{
"field_1": "string — description",
"field_2": 0,
"field_3": {
"nested_field": "string — description"
},
"field_4": ["array of strings"]
}
Request Validation Rules
| Field | Type | Required | Constraints | Default |
|---|---|---|---|---|
| field_1 | string | Yes | Max 500 chars | — |
| field_2 | integer | No | 1-100 | 10 |
| field_3.nested_field | string | Yes | Enum: ["a", "b", "c"] | — |
| field_4 | string[] | No | Max 10 items | [] |
Response Schema (Success - 200)
{
"id": "string — unique request ID",
"result": "string — AI output",
"confidence": 0.95,
"metadata": {
"model": "string",
"tokens_used": 0,
"latency_ms": 0
}
}
Error Responses
| Status Code | Error Code | Description | Action |
|---|---|---|---|
| 400 | INVALID_REQUEST | Malformed request body | Fix request and retry |
| 401 | UNAUTHORIZED | Invalid or expired API key | Refresh credentials |
| 429 | RATE_LIMITED | Too many requests | Back off and retry after Retry-After header |
| 500 | INTERNAL_ERROR | Server-side failure | Retry with exponential backoff |
| 503 | SERVICE_UNAVAILABLE | AI service temporarily down | Retry after 30 seconds |
Non-Functional Requirements
Non-Functional Requirements
Performance
| Requirement | Target | Measurement |
|---|---|---|
| Response latency (p50) | < ms | API instrumentation |
| Response latency (p95) | < ms | API instrumentation |
| Throughput | requests/second | Load testing |
| Concurrent connections | Load testing | |
| Maximum payload size | KB | Documentation |
Availability & Reliability
| Requirement | Target |
|---|---|
| Uptime SLA | % (e.g. 99.9%) |
| Planned maintenance window | |
| Maximum acceptable downtime | minutes/month |
| Retry policy | retries with exponential backoff |
| Circuit breaker threshold | consecutive failures |
| Fallback behaviour |
Security
- All communication over HTTPS/TLS 1.2+
- API keys stored in secret manager (not in code)
- PII encrypted in transit and at rest
- IP allowlisting configured:
- Request logging with PII redaction
- API key rotation schedule: every days
Data Handling
| Requirement | Detail |
|---|---|
| Data residency | Data must remain in |
| Data retention | AI provider retains data for days / does not retain |
| PII handling | |
| Logging | Log requests: Yes/No (with PII redaction) |
| Audit trail |
Instructions
How to use this template
Map the integration architecture
Start by identifying all systems involved and the data flow between them. Draw the flow diagram before writing detailed specs.
Define the API contract precisely
Specify request and response schemas with field types, constraints, and examples. Ambiguity here causes integration failures.
Specify error handling upfront
Define every error code, its meaning, and the expected consumer behaviour. Include retry policies and circuit breaker thresholds.
Set non-functional requirements with targets
Define measurable targets for latency, throughput, and availability. These become your acceptance criteria.
Watch Out
Common mistakes to avoid
FAQ
Frequently asked questions
Implement a token bucket or sliding window rate limiter on your side. Use exponential backoff with jitter for retries. For high-volume use cases, consider request queuing or pre-provisioned throughput.
Yes, for deterministic queries. If the same input always produces the same output (e.g. classification tasks), cache responses to reduce costs and latency. Use a TTL-based cache with a duration appropriate to your freshness requirements.
Use contract testing to verify API schemas, integration testing with sandbox environments, and load testing to validate performance under expected volume. Mock the AI API in unit tests.
Implement a circuit breaker pattern. After N consecutive failures, stop sending requests and return a fallback response (cached result, graceful degradation, or error message). Automatically retry after a cooldown period.
Track tokens consumed per request, requests per day, error rates, and latency. Set up alerts for unusual spikes in usage or cost. Most AI providers offer usage dashboards; supplement with your own instrumentation.
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