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Artificial Intelligence

Generative AI & LLM Engineering

Build real products on top of large language models.

Generative AI — Advance

RAG, embeddings, vector databases, agents and tool use — the LLM application stack.

60 study hours 430 pages 11 modules PDF ebook

What is inside

01 Embeddings and semantic search
02 Vector databases: pgvector, Qdrant, Pinecone
03 Retrieval-augmented generation end to end
04 Chunking, reranking and retrieval quality
05 Function calling and tool use
06 Building agents that take real actions
07 Structured output and JSON reliability
08 Streaming, latency and user experience
09 Multimodal: images, documents and audio
10 Speech to text and text to speech pipelines
11 Evaluating an LLM application

By the end you can

  • Build a RAG system over your own documents
  • Ship an AI assistant that uses tools safely
  • Measure and improve LLM application quality