Learn Retrieval Augmented Generation

Overview

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Learn all about modern search engine techniques like keyword, vector, semantic and LLM-enhanced search. In this course you'll implement different search techniques from scratch in Python everything from simple keyword search up to a fully functional Retrieval Augmented Generation (RAG) pipeline using the Gemini API.

Syllabus

Preprocessing

Normalize and clean raw corpora so they are ready for indexing and downstream retrieval tasks.

TF-IDF

Construct inverted indexes and weighting schemes so keyword search can rank documents effectively.

Keyword Search

Tune keyword retrieval with BM25 refinements and metadata boosts to improve lexical relevance.

Semantic Search

Apply embeddings, similarity metrics, and vector databases to deliver semantic retrieval and RAG responses.

Chunking

Partition documents into context-preserving segments so RAG pipelines can retrieve the right snippets efficiently.

Hybrid Search

Blend lexical and semantic scores into unified retrieval pipelines that boost ranking quality.

LLMs

Leverage large language models to expand queries, correct intent, and orchestrate retrieval workflows.

Reranking

Re-score retrieved candidates with rerankers to surface the most relevant answers.

Evaluation

Measure retrieval precision, recall, and relevance so you can systematically improve RAG performance.

Augmented Generation

Combine retrieved context with LLMs to synthesize coherent, grounded answers for end users.

Agentic

Deploy autonomous agents that iteratively refine queries and navigate complex retrieval workflows.

Multimodal

Extend RAG to images and other modalities with multimodal embeddings and cross-modal retrieval.

Taught by

Isaac Flath, Theo, Lane, Waseem, and Dan

Reviews

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