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  1. Skills Directory
  2. RAG

RAG

Intermediate

RAG (Intermediate)

At this level, learners build on basic retrieval techniques by implementing hybrid search methods, optimizing retrieval processes, and constructing full RAG pipelines.

Key Concepts and Activities

  1. Hybrid Search (Dense + Sparse Retrieval)

    • Description: Combining BM25 (sparse retrieval) and vector embeddings (dense retrieval) for improved results.

    • Reason: Hybrid search balances precision and recall, retrieving both keyword-matching and semantically relevant documents.

    • Example Task: Implement a hybrid search pipeline combining BM25 and vector search for a question-answering system.

  2. Chunking & Retrieval Optimization

    • Description: Splitting large documents into smaller retrievable chunks while preserving context.

    • Reason: Chunking ensures more relevant information is retrieved by breaking documents into manageable sections.

    • Example Task: Implement a chunking strategy that preserves context and optimizes retrieval efficiency.

  3. RAG Pipeline Construction

    • Description: Building a full retrieval-augmented generation (RAG) pipeline that retrieves context before generating a response.

    • Reason: A structured pipeline ensures reliable AI-generated responses based on retrieved knowledge.

    • Example Task: Implement a LangChain-based RAG pipeline that retrieves relevant text before passing it to an LLM.