LLM API Development 101
Learn to build production-ready LLM-powered applications using Claude API and FastAPI.
My Journey into LLM APIs
I remember my first LLM API integration. Spent 3 hours debugging why my chatbot kept timing out. The API calls worked fine in Postman, perfect in my test scripts.
In production: 504 Gateway Timeout. Every. Single. Time.
The problem? I was waiting for complete responses synchronously in FastAPI. No streaming, no async handling, blocking the entire event loop. Users saw spinning wheels, then errors.
Once I learned proper async patterns with streaming responses, timeout rate dropped from 40% to 0.2%. Response time improved by 8x.
Building LLM applications is different from traditional APIs. This series teaches you those differences through real production patterns.
What You'll Learn
This 5-part series covers everything from first API call to production deployment:
Part 1: Introduction to LLM APIs and Claude
Understanding LLM API landscape (OpenAI, Anthropic, etc.)
Setting up Claude API with Python
Making your first API call
API parameters (temperature, max_tokens, system prompts)
Cost calculation and token management
Part 2: Building FastAPI Applications with Claude
FastAPI fundamentals for LLM apps
Pydantic models for request/response validation
Integrating Claude API with async/await
Error handling and retry logic
Rate limiting client-side
Part 3: Streaming Responses and Advanced Features
Server-Sent Events (SSE) for streaming
Implementing streaming in FastAPI
Context window management
Conversation history handling
Prompt engineering patterns
Part 4: Production Patterns and Best Practices
Caching strategies (reduce costs by 60%)
Circuit breakers and fallbacks
Prompt versioning and A/B testing
Token optimization techniques
Monitoring and observability
Part 5: Deployment and Scaling
Docker containerization
Environment and secrets management
Load balancing considerations
Deploying to AWS/Azure
Production monitoring and alerting
Who This Is For
You should read this if you:
Want to integrate Claude or other LLM APIs in applications
Need to build production-grade LLM services
Are struggling with LLM API reliability and costs
Want to learn modern async Python with FastAPI
Prerequisites:
Python 3.8+ experience
Basic understanding of REST APIs
Familiarity with async/await (helpful but not required)
Tools We'll Use
Core Stack:
Python 3.11+ - Modern Python features
FastAPI - High-performance async web framework
Anthropic Claude API - Advanced LLM capabilities
Pydantic - Data validation
httpx - Async HTTP client
Production Tools:
Docker - Containerization
Redis - Caching layer
Prometheus - Metrics
Grafana - Dashboards
pytest - Testing
Why Claude API?
I've worked with OpenAI, Cohere, and Claude in production. Claude excels at:
Long context windows (200K tokens)
Following complex instructions
Structured output generation
Constitutional AI for safety
This series uses Claude, but patterns apply to any LLM API.
My Background
I've built several production LLM applications:
Customer support chatbot (handling 10K+ conversations/day)
Document analysis API (processing 50GB+ documents monthly)
Code review assistant (analyzing 1000+ PRs/week)
Every pattern in this series comes from solving real production problems.
What Makes This Different
No toy examples. Every code sample is production-ready:
Proper error handling
Async/await patterns
Cost optimization
Monitoring hooks
Security best practices
Learn from my mistakes so you don't repeat them.
Series Structure
Each part follows this format:
Personal story - Real problem I faced
Core concepts - What you need to know
Working code - Copy-paste ready examples
Production patterns - Battle-tested solutions
Common pitfalls - Mistakes to avoid
Time commitment: 30-45 minutes per part.
Let's Build!
Ready to create production LLM applications? Let's start with the fundamentals.
Start here: Part 1 - Introduction to LLM APIs and Claude
Series Navigation
Happy coding! 🚀
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