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Interactive Architecture

How AI Systems Actually Work

Follow a single request through every layer of a production AI application — from the moment you press Enter to the streamed response.

Frontend Layer
💬

User Request

▌
🌐

CDN

Static Assets
Edge Cache
⚡

Streaming Connection

WebSocket / SSE
▌
🔑

Session Check

✓
User Auth
✓
Rate Limits
✓
Permissions
API Layer
🔀

API Gateway

POST /v1/chat/completions
→ route matched → middleware chain → handler
📊

Rate Limiting & Quotas

REQUESTS0 / 100
TOKENS0 / 100K
🛡️

Security & Moderation

Prompt Injection
Content Policy
PII Detection
RAG Pipeline
RAG PIPELINE
🧱

Context Building

System PromptUser MessageChat History
🔍

Retrieval

🔎▌
📄

Your Documents

📚

Knowledge Base

📋

Relevant Context

PROMPTChunk 1Chunk 2Chunk 3Chunk 4
🧠

Semantic Cache

NEW QUESTION
PREVIOUSLY ANSWERED
SIMILARITY
0.0
⊘Cache Miss
MODEL NEEDED →
⚡Cache Hit
⚡ CACHED RESPONSE
Model Selection
◇

LLM Gateway

Routing request to optimal model provider...
🔀

Model Router

Tool Execution
🔧

Tool Calling

📡
Call API
💻
Run Code
🔎
External Search
🔗

MCP

Model Context Protocol
FilesSearchDatabaseAPIsCode
Backend Infrastructure
🏗️Backend Infrastructure
🔮

Vector Database

🗃️

PostgreSQL

🗂️USERS
🗂️CONVERSATIONS
🗂️SETTINGS
Inference
🖥️

Inference Server

Distributing request to GPU cluster...
GPU NODE
⚙️

Inference Engine

vLLM
SGLang
TRT-LLM
📦

Request Batching

BATCH
REQ
REQ
REQ
REQ
REQ
REQ
🔲

Run on GPUs

GPU 0
GPU 1
GPU 2
GPU 3
💲

Infrastructure Cost

$$$$$$$$$$$$$$$$
Observability
📡

Monitor Every Step

📝Logs
📊Traces
📈Latency
📉Token Usage
Response
✨

Response

Streaming to client
▌
End of request lifecycle

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