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Learn Retrieval Augmented Generation

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Overview

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Learn Retrieval Augmented Generation in Python by building embeddings, chunking, search, retrieval, and an LLM-powered RAG pipeline.

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.

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