AI Product Architecture Overview
AI Product Architecture describes the blueprint of microservices, databases, caching layers, and external model integrations required to run interactive, low-la...
Definition
AI Product Architecture describes the blueprint of microservices, databases, caching layers, and external model integrations required to run interactive, low-latency, stateful AI applications.
Why It Exists
Unlike traditional web systems, AI backends face high computational latency, large payload sizes (tokens), and state management complexity (conversational history). A proper architecture decoupled via queues and vector stores prevents the application server from locking up.
How It Works
AI APPLICATION ARCHITECTURE
┌────────┐
│ Client │ (Web/App UI)
└──┬─▲───┘
│ │ HTTP / WebSockets
┌──▼─┴─────────────────┐
│ API Gateway / FastAPI│ (Auth, Rate Limiting, Validation)
└──┬─▲─────────────────┘
│ │
│ │ Read / Write Context
┌──▼─┴──────────┐ ┌────────────┐
│ Orchestrator │ ◄───► │ Database │ (PostgreSQL: Metadata & Chat History)
└─┬─▲───────────┘ └────────────┘
│ │
│ ├─────────────────┐
│ │ Query Vector │ Fetch Context
┌─▼─┴──────────┐ ┌─▼────────────┐
│ LLM Provider │ │ Vector DB │ (Pinecone / PGVector: Semantic Chunks)
└──────────────┘ └──────────────┘
1. **State Ingestion**: The client makes a call.
2. **Context Enrichment (RAG)**: The backend queries a vector database using embeddings to find matching information.
3. **Prompt Composition**: The system combines System Prompts, User input, Chat History, and retrieved context.
4. **Asynchronous Execution**: If the LLM generates a long answer, the system streams tokens using Server-Sent Events (SSE) or WebSockets to reduce perceived latency.Real-World Example
ChatGPT doesn't wait for the complete answer before showing it to you. It uses streaming connection channels to push tokens to the UI as they are generated. The backend persists the chat history in a SQL database to retain conversational state across user sessions.
Python Example
Here is a modular representation of an AI application architecture containing a Chat History Store, a Mock Vector Search layer, and an LLM client:
from typing import List, Dict
class VectorDBMock:
"""Simulates a vector database containing company context files."""
def query(self, search_query: str) -> str:
# Simple semantic mock search
if "leave policy" in search_query.lower():
return "Context document: Employees get 25 days of paid annual leave."
return "No relevant documentation found."
class ChatHistoryStore:
"""Maintains user conversational state in memory."""
def __init__(self):
self.sessions: Dict[str, List[Dict[str, str]]] = {}
def add_message(self, session_id: str, role: str, content: str):
if session_id not in self.sessions:
self.sessions[session_id] = []
self.sessions[session_id].append({"role": role, "content": content})
def get_history(self, session_id: str) -> List[Dict[str, str]]:
return self.sessions.get(session_id, [])
class AIProductOrchestrator:
"""Orchestrates RAG, History, and Model completion."""
def __init__(self):
self.vector_db = VectorDBMock()
self.history = ChatHistoryStore()
def process_user_turn(self, session_id: str, user_message: str) -> str:
# Step 1: Store input
self.history.add_message(session_id, "user", user_message)
# Step 2: Context Retrieval (RAG)
context = self.vector_db.query(user_message)
# Step 3: Load History
history_context = self.history.get_history(session_id)[:-1] # Exclude latest
# Step 4: Compose Prompt Simulation
prompt = f"System: Use this context: {context}\n"
prompt += f"History: {history_context}\n"
prompt += f"User: {user_message}"
# Step 5: Generate Output (Mock LLM invocation)
response = f"Processed using context: '{context}'."
# Step 6: Store output
self.history.add_message(session_id, "assistant", response)
return response
if __name__ == "__main__":
orchestrator = AIProductOrchestrator()
sid = "user_session_123"
reply = orchestrator.process_user_turn(sid, "Tell me about the company leave policy")
print(reply)
print("Session History:", orchestrator.history.get_history(sid))Interview Questions
- Beginner: Why is streaming important in user-facing AI applications?
- Intermediate: What is the role of a Vector Database in AI Product Architecture?
- Advanced: How do you prevent SQL Injection and prompt injection attacks in a production AI system architecture?
Interview Answers
- Beginner: Streaming is crucial because LLMs take seconds to generate complete responses. Streaming returns word-by-word chunks in real-time, reducing the "Time to First Token" (TTFT) and giving the user an instant, responsive interface.
- Intermediate: A Vector Database indexes high-dimensional vectors representing text. It allows the system to search text semantically (by concept) rather than just exact keywords, bringing in precise, relevant documents to feed the LLM as grounding context.
- Advanced: Prompt injection is mitigated through input sanitization, separation of system instructions from user variables using strict JSON keys or system role flags, and utilizing secondary LLM guardrails or regex validators. SQL Injection is prevented by using standard ORMs (like SQLAlchemy or Prisma) with parameterized queries, ensuring LLM generated inputs are never executed directly as raw SQL commands.
Common Mistakes
- Lack of state awareness: Explaining architectures without mentioning how chat history or session state is maintained. LLMs themselves are stateless; state is entirely the responsibility of the application architecture.
Assignment
Draw a detailed data flowchart of an AI application that takes voice input, transcribes it, queries a Vector DB, generates a text response, and converts it back to speech. Label each endpoint interface.