LLM Production
Deploy and operate LLMs in production environments
1
Model Selection Guide
Choosing the right model
Learn how to choose between GPT-4, Claude, Gemini, Llama and other models for your use case
2
LLM Benchmarks
MMLU, HumanEval & more
Understand how to interpret benchmarks like MMLU, HumanEval, HellaSwag, and compare models
3
Vector Databases
Pinecone, Chroma, Weaviate
Learn about vector databases for semantic search and RAG applications
4
LLM Observability
Monitoring & debugging
Implement logging, tracing, and monitoring for LLM applications in production
5
Cost Optimization
Reduce API costs
Strategies for reducing LLM costs: caching, batching, model selection, and prompt optimization
6
API Integration Patterns
Streaming, retries, errors
Best practices for integrating LLM APIs: streaming responses, retry logic, rate limiting
7
LLM Deployment
FastAPI, Docker, K8s
Deploy LLM applications with FastAPI, Docker, and Kubernetes for scalability
8
Production Guardrails
Safety in production
Implement content filters, input validation, and output sanitization for safe deployments