Learning Roadmap
This roadmap documents my journey from Python FastAPI backend fundamentals to production-style AI backend engineering.
The goal is to learn how to build backend systems that integrate AI models in a clean, scalable, observable, and maintainable way.
Phase 1: FastAPI Backend Foundation
- Python project setup
- FastAPI application structure
- routing mechanism
- request and response schemas
- health check APIs
- environment-based configuration
Phase 2: Database and Persistence
- PostgreSQL integration
- SQLAlchemy async sessions
- repository pattern
- Alembic migrations
- audit logging tables
Phase 3: Authentication and Security
- user registration
- login flow
- password hashing
- JWT access tokens
- protected routes
- role-based access control
- rate limiting
Phase 4: Clean Architecture and Dependency Injection
- route layer
- use case layer
- service layer
- domain interfaces
- infrastructure adapters
- FastAPI dependency injection
- testability through dependency overrides
Phase 5: AI Backend Integration
- AI capability-based routing
- prompt building
- request guardrails
- local model support with Ollama
- cloud model support with OpenAI
- provider adapter pattern
- model registry
- inference router
- provider fallback
Phase 6: AI Response Pipeline
- raw model response handling
- response validation
- refusal detection
- hallucination guard
- quality scoring
- structured API response design
Phase 7: Performance and Cost Control
- Redis caching
- cache key design
- model timeout configuration
- prompt size limits
- request body size limits
- provider rate-limit handling
Phase 8: Observability and Debugging
- structured logging
- request IDs
- metrics endpoint
- OpenTelemetry tracing
- Jaeger tracing
- debugging AI request lifecycle
- separating model failures from infrastructure failures
Phase 9: Production Readiness
- safe error handling
- configuration validation
- secret management
- Docker Compose local stack
- health checks
- graceful startup and shutdown
- public repository readiness
Phase 10: Future RAG Integration
Next, I plan to extend the project with Retrieval-Augmented Generation.
Planned topics:
- document ingestion
- text chunking
- embeddings
- vector database
- semantic search
- grounded prompts
- citations and source-aware responses
- RAG response validation
- observability for retrieval and generation
Final Goal
The final goal is to build a practical AI backend foundation that helps developers understand how real AI applications are designed beyond a simple model API call.