SDLC & Platform Engineering for Generative AI

Deploying Generative AI applications requires transforming the standard Software Development Life Cycle (SDLC) to accommodate non-deterministic outputs, heavy model dependencies, high API latency, data ingestion complexity, and compute/cost guardrails. Platform engineering bridges this gap by providing internal developer platforms (IDPs), automated ephemeral environments, durable orchestration engines, and context-aware governance.

1. Platform Engineering Maturity Model for AI Systems

To assess and scale your organization’s AI operations, we define a 5-level maturity model for AI platform engineering:

• Level 1: Manual AI Deployments
  • Characteristics: Developers manually copy-paste prompt files, run local Python scripts to chunk and embed data, manually provision vector databases, and deploy application code via SSH or basic VM configuration.
  • Pain Points: Zero version control on prompt configurations, high risk of environment drift, zero tracing or evaluation metrics, and lack of reproducible data ingestion.
• Level 2: CI/CD Automation
  • Characteristics: Code changes trigger automated pipelines. Prompts are stored in Git. Basic unit tests run before deployment.
  • Pain Points: Shared staging databases cause data pollution during parallel development. Vector indices cannot be easily tested in isolation. No automated prompt evaluation (e.g. Promptfoo).
• Level 3: Ephemeral Environments
  • Characteristics: Pull requests trigger the creation of isolated, temporary runtime environments (e.g., Preview namespaces in Kubernetes) containing short-lived vector database instances (Qdrant) pre-seeded with test embeddings. Automated evaluations run against these environments.
  • Pain Points: Developers must manually configure database connection strings and secrets. Service dependencies must be looked up manually.
• Level 4: Internal Developer Platform (IDP)
  • Characteristics: Service catalogs (e.g., Backstage) track service dependencies (App ➔ Qdrant ➔ LLM Provider). Golden paths enable developers to provision a new microservice with pre-configured OpenTelemetry metrics, standard prompt validation blocks, and semantic caching configurations.
  • Pain Points: Provisioning new resources still requires pull requests to platform repos. Model access policies are hardcoded.
• Level 5: Self-Service AI Platform
  • Characteristics: True developer self-service. Developers use CLI commands or developer portals to deploy new prompt flows, spin up specialized agent execution runtimes, dynamically manage model access, and allocate token cost budgets using automated ABAC governance. Continuous shadow testing and automated canary model rollouts run out of the box.

📐 2. Request Lifecycle & GitOps Pipeline

An enterprise GitOps pipeline and request gateway ensures that code, prompts, database schemas, and dependencies are validated prior to production release, and then executed reliably.

3. The 7 Phases of SDLC & Platform Integration

Traditionally, the Software Development Life Cycle (SDLC) consists of 7 core phases:

1. Planning: Establishing project goals, scope, requirements, timelines, resource allocation, and cost-benefit analysis.
2. Requirements Analysis: Gathering detailed requirements from stakeholders, customers, and market research. Creating Software Requirement Specification (SRS) documents.
3. Design: Creating system architecture, data flows, selecting technology stacks, and defining component interactions.
4. Implementation/Development: Writing actual code, integrating APIs, and configuring models.
5. Testing: Conducting unit testing, integration testing, user acceptance testing (UAT), security scanning, and model evaluations.
6. Deployment: Packaging code, configuring environments, and releasing the application to users.
7. Maintenance: Monitoring performance, fixing bugs, managing model drift, updating prompts, and handling user feedback.

Platform engineering integrates specific tools across these phases to optimize developer velocity and ensure system reliability:

4. Service Catalog Integration

Role in SDLC: Planning, Design, Development, Maintenance

Service catalogs (like Spotify’s Backstage or Port) serve as a centralized metadata repository. They provide:

• Discovery and Documentation: A single source of truth for all services, APIs, dependencies, and model endpoints.
• Onboarding Support: Help developers understand the tech stack and service relationships during planning and development.
• Incident Management: Enable quick identification of service owners and dependencies during outages or model failures.
• Standards Tracking: Monitor compliance with organizational standards (security, evaluations, cost logs).

Backstage catalog-info.yaml Example

For a GenAI service relying on a Qdrant vector database, the service catalog metadata must explicitly define dependencies so platform engineers can trace the impact of a database update or model retirement:

apiVersion: backstage.io/v1alpha1
kind: Component
metadata:
  name: rag-search-service
  description: "Enterprise Search Service using Qdrant Vector Database"
  annotations:
    backstage.io/techdocs-ref: dir:.
    prometheus.io/scrape: "true"
spec:
  type: service
  lifecycle: production
  owner: ai-search-team
  system: enterprise-knowledge-base
  dependsOn:
    - resource:qdrant-cluster-prod
    - api:openai-gpt-4o-api
---
apiVersion: backstage.io/v1alpha1
kind: Resource
metadata:
  name: qdrant-cluster-prod
  description: "Production Qdrant Vector Database Cluster"
spec:
  type: database
  owner: database-ops-team

5. Developer Self-Service

Role in SDLC: Development, Testing, Deployment

Developer self-service capabilities reduce bottlenecks by letting developers provision infrastructure independently without waiting for Ops approval:

• Development Phase: Developers provision sandboxes, vector indices, and LLM API access profiles independently.
• Testing Phase: Automated generation of isolated preview databases and mocks for LLM endpoints.
• Deployment Phase: Standardized, pre-configured GitHub Actions or GitLab CI templates containing testing, evaluation, and deployment steps.

6. Ephemeral Environments & Vector DB Seeding

Role in SDLC: Development, Testing

Ephemeral environments are temporary, isolated workspaces spun up on-demand (e.g., on a pull request) and deleted when the task is done. This eliminates testing bottlenecks on shared staging environments and reduces infrastructure costs.

To evaluate a RAG pipeline on a pull request, the ephemeral environment must spin up a local instance of the vector database and seed it with realistic test data.

Ephemeral Infrastructure: docker-compose.yaml

version: '3.8'
services:
  qdrant-ephemeral:
    image: qdrant/qdrant:v1.9.0
    ports:
      - "6333:6333"
      - "6334:6334"
    environment:
      - QDRANT__SERVICE__ENABLE_STATIC_CONTENT=0
    volumes:
      - qdrant_test_data:/qdrant/storage
 
volumes:
  qdrant_test_data:

Ephemeral Seeding Script: seed_qdrant.py

This script runs in the CI/CD pipeline right after the ephemeral container starts, populating Qdrant with test chunks and embeddings to prepare for evaluation tests:

import os
from qdrant_client import QdrantClient
from qdrant_client.http import models
 
# Initialize Qdrant client pointing to local ephemeral instance
client = QdrantClient(host="localhost", port=6333)
 
COLLECTION_NAME = "test_knowledge_base"
 
def seed_database():
    # 1. Create Collection with Cosine distance metric for 1536-dim embeddings
    client.recreate_collection(
        collection_name=COLLECTION_NAME,
        vectors_config=models.VectorParams(
            size=1536,
            distance=models.Distance.COSINE
        )
    )
    
    # 2. Mock payload and embeddings (e.g. generated via local FastEmbed or test suite)
    test_documents = [
        {"id": 1, "text": "Deploying Temporal workflows requires a worker pool.", "vec": [0.015] * 1536},
        {"id": 2, "text": "Qdrant collection parameters support cosine similarity.", "vec": [-0.022] * 1536},
    ]
    
    # 3. Upsert into Ephemeral Qdrant instance
    client.upsert(
        collection_name=COLLECTION_NAME,
        points=[
            models.PointStruct(
                id=doc["id"],
                vector=doc["vec"],
                payload={"page_content": doc["text"]}
            )
            for doc in test_documents
        ]
    )
    print(f"Successfully seeded Qdrant collection: {COLLECTION_NAME}")
 
if __name__ == "__main__":
    seed_database()

7. Feature Flags in GenAI

Role in SDLC: Development, Testing, Deployment, Maintenance

Feature flags decouple code deployment from feature release:

• Development Phase: Supports trunk-based development by keeping in-progress features hidden behind toggles.
• Testing Phase: Enables canary testing and shadow deployments of new models/prompts in production.
• Deployment Phase: Gradual rollouts (e.g. 5% ➔ 25% ➔ 50% ➔ 100%) and instant rollbacks.
• Maintenance Phase: Instant cut-off in case of model outages, downstream API failures, or token cost overruns.

For detail on implementation, refer to the Feature Flags Playbook.

8. Durable Workflow Execution (Temporal)

Role in SDLC: Implementation, Deployment, Maintenance

Durable workflow engines like Temporal provide fault-tolerant orchestration for complex, multi-step processes. Unlike standard async tasks (e.g. Celery), Temporal offers:

• Crash-proof execution: Automatically checkpoints state at every step and resumes execution from the last step in case of system failures, network drops, or worker crashes.
• Deterministic Replay: Replays workflows deterministically, ensuring that completed actions are not re-executed.
• Automatic Retries with Backoff: Retries failing activities (like rate-limited LLM calls or vector DB writes) with configurable policy parameters.
• No Time Limits: Workflows can execute for minutes, days, or months.

Production Pattern: RAG Ingestion Pipeline with Temporal (Python SDK)

A production-grade RAG pipeline requires chunking a file, generating embeddings, and upserting vectors to Qdrant. If the embeddings API fails halfway through, a standard script restarts from scratch, resulting in duplicate costs and half-populated states. Temporal guarantees that the ingestion pipeline executes exactly-once.

Workflow Definition: workflows.py

from datetime import timedelta
from temporalio import workflow
from temporalio.common import RetryPolicy
 
# Import activity definitions
with workflow.unsafe.imports_passed_through():
    from activities import chunk_document, generate_embeddings, upsert_to_qdrant
 
@workflow.def
class DocumentIngestionWorkflow:
    @workflow.run
    async def run(self, file_path: str) -> dict:
        # Define retry policy for network-bound operations (LLM APIs, Qdrant)
        standard_retry = RetryPolicy(
            initial_interval=timedelta(seconds=2),
            backoff_coefficient=2.0,
            maximum_interval=timedelta(seconds=30),
            maximum_attempts=5
        )
 
        # Step 1: Chunk Document (CPU bound)
        chunks = await workflow.execute_activity(
            chunk_document,
            file_path,
            start_to_close_timeout=timedelta(minutes=5)
        )
 
        # Step 2: Generate Embeddings (Network/API bound with retries)
        embeddings = await workflow.execute_activity(
            generate_embeddings,
            chunks,
            start_to_close_timeout=timedelta(minutes=10),
            retry_policy=standard_retry
        )
 
        # Step 3: Upsert into Qdrant (Network bound with retries)
        result = await workflow.execute_activity(
            upsert_to_qdrant,
            embeddings,
            start_to_close_timeout=timedelta(minutes=5),
            retry_policy=standard_retry
        )
 
        return {"status": "success", "processed_records": len(chunks)}

Activities Definition: activities.py

import os
from temporalio import activity
from qdrant_client import QdrantClient
from qdrant_client.http import models
import openai
 
openai_client = openai.OpenAI(api_key=os.environ.get("OPENAI_API_KEY"))
qdrant_client = QdrantClient(host="localhost", port=6333)
 
@activity.defn
async def chunk_document(file_path: str) -> list[str]:
    # Simulate reading and chunking logic
    activity.heartbeat("Reading file")
    # In practice, load pdf/text and parse
    return [
        "Chunk 1: Platform engineering builds internal platforms.",
        "Chunk 2: Temporal workflows are stateful and crash-proof."
    ]
 
@activity.defn
async def generate_embeddings(chunks: list[str]) -> list[dict]:
    results = []
    for idx, chunk in enumerate(chunks):
        activity.heartbeat(f"Embedding chunk {idx}/{len(chunks)}")
        
        # Invoke embeddings API
        response = openai_client.embeddings.create(
            input=chunk,
            model="text-embedding-3-small"
        )
        vector = response.data[0].embedding
        results.append({"text": chunk, "vector": vector, "id": idx})
        
    return results
 
@activity.defn
async def upsert_to_qdrant(embeddings: list[dict]) -> str:
    points = [
        models.PointStruct(
            id=item["id"],
            vector=item["vector"],
            payload={"page_content": item["text"]}
        )
        for item in embeddings
    ]
    
    qdrant_client.upsert(
        collection_name="production_knowledge_base",
        points=points
    )
    return "Upsert completed successfully"

9. Governance & Access Control

Role in SDLC: Design, Implementation, Deployment, Maintenance

Governance ensures that AI applications adhere to security policies, regulatory guidelines, and budgeting limits:

• Design Phase: Defining security boundaries, model access lists, and privacy boundaries (e.g. data masking).
• Implementation Phase: Developers write code respecting access limits. Implementing RBAC (Role-Based Access Control) and ABAC (Attribute-Based Access Control) to gate models.
• Deployment Phase: Enforcing branch protection rules, automated security scanning (SAST/DAST), and prompt injections checks.
• Maintenance Phase: Continuous logging of model outputs, token consumption, drift detection, and cost aggregation.

Context-Aware ABAC Governance Decorator (FastAPI)

Access to premium models (e.g., GPT-4o, Claude 3.5 Sonnet) must be dynamically gated using attributes (user department, token budget, request context) rather than simple roles. This avoids resource exhaustion and controls operating expenditure.

import os
from functools import wraps
from fastapi import HTTPException, Security, status
from fastapi.security import APIKeyHeader
 
API_KEY_HEADER = APIKeyHeader(name="X-API-KEY", auto_error=True)
 
# Mock database tracking token costs and user attributes
USER_METRICS_DB = {
    "dev-api-key-123": {
        "user_id": "user_alpha",
        "department": "Engineering",
        "monthly_spend_usd": 120.50,
        "spend_limit_usd": 150.00,
        "allowed_models": ["gpt-4o-mini", "claude-3-5-haiku"]
    },
    "biz-api-key-456": {
        "user_id": "user_beta",
        "department": "Research",
        "monthly_spend_usd": 850.00,
        "spend_limit_usd": 1000.00,
        "allowed_models": ["gpt-4o", "claude-3-5-sonnet", "gpt-4o-mini"]
    }
}
 
def verify_abac_rules(target_model: str):
    """Decorator to enforce context-aware ABAC governance rules on LLM calls."""
    def decorator(func):
        @wraps(func)
        async def wrapper(*args, **kwargs):
            # Extract api key from FastAPI dependency injection
            api_key = kwargs.get("api_key")
            if not api_key or api_key not in USER_METRICS_DB:
                raise HTTPException(
                    status_code=status.HTTP_401_UNAUTHORIZED,
                    detail="Invalid or missing API key"
                )
                
            user_profile = USER_METRICS_DB[api_key]
            
            # Rule 1: Check Model Whitelist
            if target_model not in user_profile["allowed_models"]:
                raise HTTPException(
                    status_code=status.HTTP_403_FORBIDDEN,
                    detail=f"Access Denied: Model '{target_model}' is not in allowed models for department '{user_profile['department']}'"
                )
            
            # Rule 2: Check Monthly Cost Budget Exceeded
            if user_profile["monthly_spend_usd"] >= user_profile["spend_limit_usd"]:
                raise HTTPException(
                    status_code=status.HTTP_402_PAYMENT_REQUIRED,
                    detail=f"Access Denied: Monthly spend limit of ${user_profile['spend_limit_usd']:.2f} exceeded. Current: ${user_profile['monthly_spend_usd']:.2f}"
                )
                
            return await func(*args, **kwargs)
        return wrapper
    return decorator
 
# Example usage in FastAPI Router
# @app.post("/generate")
# @verify_abac_rules(target_model="claude-3-5-sonnet")
# async def generate_text(request: PromptRequest, api_key: str = Security(API_KEY_HEADER)):
#     ...

10. Summary & Checklist

SDLC AI Integration Checklist

• Register all vector database resources (Qdrant) and LLM endpoints in the developer service catalog.
• Configure pull-request triggers to spin up ephemeral environments, database instances, and mock LLM configurations.
• Use a seeding script to populate test collections in the ephemeral vector DB before running integration test steps.
• Gate long-running pipelines (like batch processing or ingestion) behind durable execution workflows (Temporal).
• Enforce dynamic cost limit checks and model routing permissions at the API Gateway using context-aware ABAC.