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AI Agents · Lesson

What RAG Solves (Knowledge Cut-off, Hallucinations)

RAG retrieves relevant context at query time so the LLM cites real sources instead of making facts up.

What RAG Solves (Knowledge Cut-off, Hallucinations) is a free AI Agents lesson on CoddyKit — lesson 1 of 4. You can read the complete lesson below for free — then practise it hands-on in the browser with a built-in code editor and a 24/7 AI tutor. It is part of the AI Agents learning path, one of 4 lessons in the course, and your progress syncs across the web and the CoddyKit app.

Two Problems With Plain LLMs

Even the best LLMs have two structural problems:

  1. Knowledge cut-off — they only know what was in their training data. Ask GPT-4 about an event yesterday and it has no idea.
  2. Hallucination — when the model does not know, it confidently makes things up.

What RAG Is

Retrieval-Augmented Generation (RAG) means:

  1. Search a knowledge base for relevant chunks
  2. Stuff those chunks into the prompt
  3. Ask the LLM to answer using ONLY those chunks

The model becomes a "reader" over external knowledge instead of relying on its frozen weights.

A Concrete Example

User: "What is our return policy for opened electronics?"

  1. Search company KB for "return policy electronics" — get 3 paragraphs from the help docs
  2. Build prompt: "Using the context below, answer: ..."
  3. Model produces a faithful answer with citations

Why RAG Beats Fine-Tuning

For factual knowledge, RAG > fine-tuning because:

  • Update KB without re-training
  • Per-user / per-tenant data is trivially separated
  • Citations are possible (you know WHICH chunk produced the answer)
  • Cheaper to maintain

Why RAG Reduces Hallucination

If the model is instructed to answer ONLY from provided context, and the context contains the truth, the model is far more likely to be correct than relying on memory.

It is not perfect — models still occasionally invent — but it cuts hallucination dramatically.

The Three Stages

Every RAG system has three stages:

  1. Ingestion (offline) — chunk docs, embed, store
  2. Retrieval (online) — embed query, find top-K chunks
  3. Generation (online) — LLM produces answer using retrieved chunks

Naive RAG vs Advanced RAG

Naive RAG (5-line prototype):

  • Fixed-size chunking
  • Single-vector retrieval
  • Stuff top-K into prompt

Advanced RAG adds re-ranking, query rewriting, HyDE, multi-vector, evaluation — each adds quality but also complexity.

When RAG Helps

  • Company knowledge bases / docs
  • Customer support over a product manual
  • Long-tail factual Q&A
  • Personal assistants over user data

When RAG Hurts

  • Simple chit-chat (no facts needed)
  • Tasks needing reasoning, not facts
  • When the corpus is small enough to fit in context anyway

The Prompt Template

A typical RAG prompt looks like this:

prompt = f'''
Answer the user\'s question using ONLY the context below.
If the answer is not in the context, say 'I do not know'.

Context:
{retrieved_chunks}

Question:
{user_question}

Answer:
'''

Citations

Format chunks with source IDs and ask the model to cite them:

context = '\n'.join(
    f'[doc {i+1}] {chunk.text}'
    for i, chunk in enumerate(chunks)
)
# Then ask: 'Cite the relevant [doc N] for each claim.'

RAG Goal

What does RAG primarily address?

Recap

RAG turns LLMs into open-book test-takers. Next: how to actually chunk documents for retrieval.

Frequently asked questions

Is the “What RAG Solves (Knowledge Cut-off, Hallucinations)” lesson free?

Yes — the full text of “What RAG Solves (Knowledge Cut-off, Hallucinations)” is free to read here on the web, and the AI Agents course includes 4 lessons in total. To practise it interactively (a built-in code editor and a 24/7 AI tutor) and unlock the rest of the AI Agents course, upgrade to CoddyKit PRO.

What will I learn in “What RAG Solves (Knowledge Cut-off, Hallucinations)”?

RAG retrieves relevant context at query time so the LLM cites real sources instead of making facts up. You practise AI Agents with hands-on code you run directly in the browser, and a 24/7 AI tutor answers your questions as you work through the lesson.

Do I need any experience to start AI Agents?

No prior experience is required. AI Agents on CoddyKit is structured for beginners through advanced learners; this is — lesson 1 of 4, so you can start here or from the beginning and move at your own pace.

How long does the “What RAG Solves (Knowledge Cut-off, Hallucinations)” lesson take?

Most CoddyKit lessons take about 5–10 minutes. Each one is bite-sized and interactive, so you make steady progress and pick up exactly where you left off across the web and the app.

Can I write and run code in this AI Agents lesson?

Yes. Every AI Agents lesson includes a built-in code editor, so you write and run real code right in your browser and get instant AI feedback — no local setup required.

All lessons in this course

  1. What RAG Solves (Knowledge Cut-off, Hallucinations)
  2. Chunking Strategies (Fixed, Sentence, Semantic)
  3. Indexing a Document Set
  4. Building a Naive RAG with FAISS or Chroma
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