Chapter 3: Building Custom GPT Products
From Model to Product
Considerations for building production AI applications.
Product Architecture
Key Considerations
| Aspect | Consideration |
|---|---|
| Latency | Target < 2s for first token |
| Throughput | Concurrent users supported |
| Cost | Per-query cost optimization |
| Safety | Content filtering |
| Monitoring | Logging and observability |
API Design
from fastapi import FastAPI
from pydantic import BaseModel
app = FastAPI()
class ChatRequest(BaseModel):
message: str
history: list = []
@app.post("/chat")
async def chat(request: ChatRequest):
response = generate_response(
request.message,
request.history
)
return {"response": response}
Iteration Cycle
- Deploy MVP with basic model
- Collect feedback from users
- Analyze failures and edge cases
- Curate new training data
- Fine-tune improved model
- Evaluate and deploy
- Repeat
Course Complete! 🎉
You've learned everything needed to fine-tune and deploy custom LLMs. Continue practicing and building!