01Module 02 - Python for LLM Engineering OverviewPython patterns for building production LLM applications - API integration, streaming, prompt engineering, token management, tool use, and vector search.02Calling LLM APIsProduction patterns for calling LLM APIs - authentication, retry logic, rate limiting, error handling, async calls, and the Anthropic and OpenAI Python SDKs.03Streaming LLM ResponsesStreaming LLM output in Python - server-sent events, async generators, FastAPI streaming endpoints, and building real-time chat UIs.04Prompt Templates in PythonBuilding maintainable prompt systems in Python - template engines, versioning, testing prompts, few-shot construction, and prompt injection defense.05Token Counting and Context ManagementTiktoken, tokenisation internals, context window management, sliding window strategies, and building cost-aware LLM applications.06Tool Use from PythonBuilding LLM tool use systems in Python -- function calling, tool schemas, execution loops, error handling, and multi-step agent patterns.07Python for Vector SearchEmbeddings, vector databases, similarity search, RAG pipelines, and production vector search in Python with FAISS, Chroma, Pinecone, and pgvector.
