Building RAG Agents with LLMs

About this course

Build retrieval-augmented generation agents that answer from your own documents. You will design pipelines with LangChain, Gradio and LangServe, manage dialogue state, use embeddings and vector stores, add guardrails and evaluate the results.

What you'll learn

• Compose an LLM system that can interact predictably with a user by leveraging internal and external reasoning components.
• Design a dialog management and document reasoning system that maintains state and coerces information into structured formats.
• Leverage embedding models for efficient similarity queries for content retrieval and dialog guardrailing.
• Implement, modularize, and evaluate a retrieval-augmented generation agent that can answer questions about the research papers in its dataset without any fine-tuning.

Curriculum

Who it's for

Developers and data scientists building knowledge assistants and document question-answering.

Before you start

• Introductory deep learning, with comfort with PyTorch and transfer learning preferred. Content covered by "Getting Started with Deep Learning" or "Fundamentals of Deep Learning" courses or similar experience is sufficient.
• Intermediate Python experience, including object-oriented programming and libraries. Content covered by Python Tutorial (w3schools.com) or similar experience is sufficient.