We use cookies to ensure you have the best browsing experience on our website. Please read our cookie policy for more information about how we use cookies.
  1. Skills Directory
  2. RAG

RAG

Basic

RAG (Basic)

This level covers foundational skills required to implement basic retrieval-augmented generation (RAG) systems. The focus is on understanding core components like tokenization, embedding generation, and simple vector search techniques.

Key Concepts and Activities

  1. Tokenization & Text Preprocessing

    • Description: Breaking down text into meaningful units to improve retrieval and generation.

    • Reason: Proper tokenization ensures efficient text processing, improving search accuracy and generation quality.

    • Example Task: Implement a tokenizer that processes raw text into tokens while handling stopwords, punctuation, and stemming.

  2. Embedding Generation & Storage

    • Description: Converting text into high-dimensional vectors for similarity search.

    • Reason: Embeddings capture semantic meaning, enabling better information retrieval.

    • Example Task: Generate sentence embeddings using a transformer model and store them in a vector database.

  3. Basic Vector Search & Retrieval

    • Description: Storing and retrieving embeddings using a vector database.

    • Reason: Vector search enables efficient similarity-based retrieval, improving response relevance.

    • Example Task: Implement a basic vector search pipeline using FAISS to retrieve similar documents based on cosine similarity.