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    1 min read 5 stepsMarch 16, 2026Verified March 2026

    What Is RAG? Retrieval-Augmented Generation Explained

    Understand how RAG works and why it makes AI responses more accurate and grounded in facts.

    At a Glance

    Category
    AI In Depth
    Difficulty
    Advanced
    Read Time
    1 min read
    Steps
    5
    Topics covered
    advanced
    rag
    architecture
    ai-engineering

    In this guide (5 steps):

    1. 1.The problem RAG solves
    2. 2.How RAG works
    3. 3.Embeddings in plain language
    4. 4.Vector databases
    5. 5.When to use RAG
    1

    The problem RAG solves

    ~15s
    LLMs have a knowledge cutoff and can hallucinate. RAG lets AI search your own documents first, then answer based on actual data.
    2

    How RAG works

    ~15s
    Step 1: Convert your docs into embeddings (mathematical representations). Step 2: When asked a question, find relevant chunks. Step 3: Send those chunks + the question to the LLM.
    3

    Embeddings in plain language

    ~15s
    Embeddings turn text into numbers that capture meaning. "Happy" and "joyful" have similar numbers, so AI knows they're related.
    4

    Vector databases

    ~15s
    Pinecone, Weaviate, and Chroma store embeddings for fast retrieval. They're like search engines for meaning, not just keywords.
    5

    When to use RAG

    ~15s
    Customer support bots, internal knowledge bases, research assistants, and any application where accuracy matters more than creativity.

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    What Is RAG? Retrieval-Augmented Generation Explained — Step-by-Step Guide | TekSure