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Build 2.139 · 09 Nov 2026, 10:59 UTC

    HackIndia
    HackathonsNewsInternshipsSkillsGrowth
    Working towardsRAG
    0%

    0 of 9 steps done · +100 XP · +50 SHARP when it is yours

    1. 1Why retrieval beats memory4 min · +10 XP · +5 SHARP
    2. 2Prepare documents for search5 min · +10 XP · +5 SHARP
    3. 3Create embeddings and a vector index4 min · +10 XP · +5 SHARP
    4. 4Put evidence into the prompt5 min · +10 XP · +5 SHARP
    5. 5Cite sources users can verify5 min · +10 XP · +5 SHARP
    6. 6Measure grounded answers5 min · +10 XP · +5 SHARP
    7. 7Ship the repo end to end4 min · +10 XP · +5 SHARP
    8. The check10 questions, 7 to pass · +10 SHARP
    9. Hand in the taskA project that uses the skill
    10. Skill and certificate+100 XP · +50 SHARP
    Lesson 1 of 7 4 min

    RAG

    Why retrieval beats memory

    • +10 XP for this lesson
    • +100 XP when the skill is proved
    • 2 learning it right now
    In 30 seconds
    • Explain why LLM memory is unsafe for document QA.
    • Describe how retrieval grounds answers in your files.
    Plan a tiny QA repo around evidence, not guessing.

    The problem

    A chat model can sound confident even when it has not seen your document. If you ask about your college rulebook, internship policy, or a startup pitch deck, the model may answer from general patterns instead of your file.

    For a document QA app, that is not enough. Your user needs answers that come from the uploaded documents and can be checked.

    Key point

    One idea

    Do not ask the model to remember your documents. Retrieve the relevant parts first, then ask the model to answer only from those parts.

    Memory vs retrieval

    Model memory means the information learned during training, plus whatever is in the current chat. It is broad, but not reliable for your private PDFs, notices, invoices, resumes, or project docs.

    Retrieval means your app searches your own document collection at answer time. It finds small pieces of text that match the question, puts them into the prompt, and asks the model to answer using that evidence.

    Example

    Tiny prompt difference

    A memory-only prompt asks for an answer directly. A retrieval prompt includes evidence and clear rules.

    Memory-only:
    Question: What is the late submission penalty?
    Answer the question.
    
    Retrieval-grounded:
    Evidence:
    [doc: assignment_policy.txt, lines 12-14]
    Late submissions lose 10% marks per day. Submissions more than 5 days late are not accepted.
    
    Question: What is the late submission penalty?
    Answer using only the evidence. If the evidence is not enough, say you do not know.
    Common mistake

    Common mistake

    Many first RAG projects upload a PDF, ask the model questions, and assume the answer is grounded. That is not a QA system. That is a chat prompt with a file nearby.

    A recruiter inspecting your repo should see the flow: documents, chunks, search, evidence in prompt, answer, citations, and a small grounding check.

    Tip

    Think like an examiner

    In an exam, an answer without a source is weak. In document QA, the source is the retrieved text. Your app should make it easy to ask: where did this answer come from?

    This is why your final repo will include sample documents, example questions, cited answers, and a short evaluation. You are proving that the app can answer from evidence.

    Your to-do

    Basic RAG flow

    Do each one yourself, then tap it to tick it off. The ticks are only a checklist for you: they are not marked or scored.

    0 of 5 done

    Your to-do

    Do this now

    Do each one yourself, then tap it to tick it off. The ticks are only a checklist for you: they are not marked or scored.

    0 of 5 done

    Quick check

    0 of 2 right
    1.You ask about a college policy PDF. Why is model memory alone unsafe?
    2.What is the core flow of a grounded document QA app?

    Answer the quick check to finish this lesson.

    Skip to lesson 2 →
    Working towards
    RAG +100 XP

    0 of 7 lessons done · proved by a project that uses the skill

    See the track