Building a Production-Style AI Backend
- 4 hours ago
- 5 min read
Course: AI Backend Engineering with FastAPI
Assignment Type: Capstone Implementation + Architecture Report
Difficulty: Medium → Advanced
Estimated Effort: 10–15 hours
Submission Platform: LMS (Moodle / Canvas / Blackboard)
Assignment Overview
In this assignment, you will design and implement a production-style AI backend API using the concepts introduced throughout this course.
You will build a modular FastAPI system that integrates:
LLM inference
Retrieval-Augmented Generation (RAG)
Background processing
Streaming responses
Logging and monitoring
Clean architecture design
The goal is to simulate how real-world AI systems are engineered, rather than building a simple demo endpoint.
Your system should demonstrate scalable backend design, modularity, and observability.
Learning Objectives
By completing this assignment, you should be able to:
Design a clean API architecture using FastAPI.
Implement structured request validation using Pydantic schemas.
Build service layers to separate business logic from routing.
Implement async endpoints for scalable AI APIs.
Integrate a Retrieval-Augmented Generation (RAG) pipeline.
Implement streaming responses for real-time LLM output.
Add background task processing for long-running operations.
Implement logging and middleware for observability.
Demonstrate production-ready API practices.
Assignment Scenario
You are tasked with building an API backend for a hypothetical product:
AI Knowledge Assistant
The assistant should be able to:
Accept user queries
Retrieve relevant information from a knowledge base
Generate responses using an LLM
Optionally stream responses
Perform background processing tasks
Log requests and responses for monitoring
System Requirements
Your project must implement the following core features.
Required API Endpoints
AI Assistant Endpoint
Endpoint
POST /ai-assistant
Expected Capabilities
The endpoint must:
Accept structured messages
Optionally retrieve context (RAG)
Call an LLM service
Support streaming responses
Use background tasks for secondary processing
Example Request
{
"messages": [
{"role": "user", "content": "Explain FastAPI"}
],
"temperature": 0.7,
"stream": false,
"use_rag": true,
"use_ml": false
}
Expected Behavior
The system should:
Validate input using Pydantic schemas
Extract the latest user message
Perform optional retrieval
Construct a prompt
Call an LLM service
Return generated response
Required Architecture
Your project must follow a layered architecture.
Required folder structure:
app/
main.py
routers/
services/
schemas/
core/
Routers
Responsible for:
Handling HTTP requests
Calling service layer
Returning responses
Example: app/routers/assistant.py
Services
Services must contain:
LLM interaction logic
RAG retrieval
ML model calls
Background jobs
Example:
app/services/llm.py
app/services/vector_store.py
app/services/ml_model.py
app/services/background_jobs.py
Schemas
Use Pydantic models to validate input.
Example schema:
AssistantRequest
Message
Schemas must include:
message structure
temperature parameter
feature toggles
RAG Pipeline Requirement
You must implement a simple retrieval mechanism.
The system should:
Accept user query
Retrieve relevant documents
Inject retrieved context into the prompt
This may be implemented using:
A mock vector store
Static knowledge documents
Simulated retrieval logic
Streaming Responses
Your system must support optional streaming responses.
If:
stream = true
Then:
The API should return a StreamingResponse
Tokens/words should be streamed gradually
Background Processing
You must implement at least one background task.
Example tasks:
Logging user queries
Saving conversation history
Running analytics on user input
Use:
BackgroundTasks
Logging and Middleware
Your API must implement logging.
Logs should capture:
Request method
Endpoint accessed
Request duration
Errors if any
Middleware should:
generate request IDs
measure latency
Error Handling
You must implement custom error handling for:
Validation errors
HTTP exceptions
Responses should be structured and user-friendly.
Async Programming
Your LLM service must use: async def to simulate or implement asynchronous model calls.
Documentation
Your API must expose documentation using:
Swagger UI
Accessible via:
/docs
Code Quality Expectations
Your code must demonstrate:
clean modular structure
meaningful variable names
clear separation of concerns
comments where necessary
Avoid placing all logic inside a single file.
Optional Bonus Features (Extra Credit)
Students may earn bonus marks by implementing additional features such as:
Rate limiting
API key authentication
Integration with a real LLM API
Real vector database (FAISS, Pinecone, Chroma)
Request caching
Celery-based task queue (optional)
Deliverables
You must submit the following:
Source Code
Upload a ZIP archive containing the full project.
Folder structure must be preserved.
README File
Include a README.md with:
project description
installation steps
how to run the server
API endpoint explanation
example request
Architecture Explanation (1–2 pages)
Submit a short document explaining:
system architecture
design decisions
how services interact
how scalability could be improved
Accepted formats:
PDF
DOCX
Demonstration Evidence
Students must include screenshots of:
API running
Swagger documentation
Successful API request
Streaming response example
Background task logs
Submission Instructions (LMS)
Submit the following files via the LMS platform.
Upload:
architecture.pdf
screenshots folder
File naming convention:
AI_Backend_Assignment_<StudentID>.zip
Academic Integrity
Students must:
Write their own code
Avoid copying from peers
Cite external libraries used
Use of AI tools is allowed only as a learning aid, not as a replacement for understanding.
All submissions may be checked for similarity.
Evaluation Rubric
Criteria | Marks |
Architecture & Structure | 20 |
API Functionality | 20 |
RAG Integration | 15 |
Streaming Implementation | 10 |
Background Tasks | 10 |
Logging & Observability | 10 |
Code Quality | 10 |
Documentation | 5 |
Total: 100 Marks
Deadline
Submission Deadline: [Instructor to specify]
Late submissions may incur penalties according to course policy.
Final Advice for Students
Do not rush to code immediately.
First:
Design your architecture
Create folder structure
Implement services step by step
Focus on clarity and modular design rather than complexity.
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