Building LLM Applications with Tools and Retrieval
Building an LLM application requires more than a single prompt: it means calling model APIs, controlling output, connecting tools, and grounding answers in your own data. This course shows you how to build LLM applications in Python that use tool calling and retrieval-augmented generation (RAG). Through follow-along demos, you will call multiple LLM providers from one codebase, compose chains in LangChain, index documents with LlamaIndex, and store embeddings in ChromaDB, Pinecone, and Weaviate. By the end of this course, you will be able to: • Integrate LLM provider APIs and estimate toke
Skills you'll learn
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