Welcome: Why LLMs Need Knowledge Bases

You’ve used ChatGPT or Claude to answer questions. They’re impressive — until they confidently tell you something completely wrong. That’s hallucination, and it’s the #1 reason businesses can’t trust LLMs with their actual data.

RAG fixes this. Here’s how.

The Three LLM Problems

Problem 1: Hallucination

LLMs generate plausible-sounding text, not verified facts. Ask “What was Acme Corp’s Q3 revenue?” and the LLM will invent a convincing number — because it doesn’t have Acme Corp’s data, and it doesn’t know what it doesn’t know.

Problem 2: Knowledge Cutoff

LLMs are trained on data up to a specific date. Ask about events after that date and you get outdated or fabricated answers. Your company’s latest product launch, last week’s market data, yesterday’s policy changes — all invisible to the base model.

Problem 3: No Domain Knowledge

A general-purpose LLM doesn’t know your company’s internal processes, your product specifications, your HR policies, or your customer data. It answers from general knowledge, which might directly contradict your specific reality.

How RAG Solves All Three

RAG — Retrieval-Augmented Generation — connects the LLM to your actual data at query time:

User asks: "What was our Q3 revenue?"
                    ↓
[Retrieval] Search your financial database
            → Finds: "Q3 2025 revenue: $8.7M"
                    ↓
[Generation] LLM receives: "Using this context: Q3 2025
             revenue was $8.7M. Answer the user's question."
                    ↓
LLM responds: "Your Q3 2025 revenue was $8.7 million,
              based on the quarterly financial report."

The LLM doesn’t need to know the answer — it just needs to read the answer from your documents and present it clearly.

✅ Quick Check: A law firm wants their AI assistant to answer questions about case precedents. Should the LLM memorize all case law (fine-tuning), or should it search the firm’s case database for each question (RAG)? (Answer: RAG. Case law is updated constantly with new rulings. RAG lets the firm add new cases to their knowledge base immediately. It also provides citation — the AI can point to the exact case it’s referencing. Fine-tuning would require expensive retraining for every new ruling and couldn’t cite specific sources.)

RAG vs. Alternatives

RAG is the most practical starting point for most use cases. It’s faster to implement, easier to update, and provides built-in citation capabilities.

What You’ll Learn

This course takes you from understanding RAG conceptually to designing production RAG systems:

1. Architecture — The three stages: indexing, retrieval, generation
2. Document processing — Chunking strategies, metadata extraction, handling different formats
3. Embeddings and vector databases — How semantic search works, choosing the right tools
4. Retrieval strategies — Hybrid search, reranking, query rewriting
5. Generation and grounding — Prompting for faithfulness, citation, and source attribution
6. Evaluation — Measuring and improving RAG quality with RAGAS metrics

How This Course Works

Each lesson builds on the previous one, following the data flow through a RAG system. You’ll learn concepts through concrete examples and apply them through exercises. No coding is required for the core material.

Optional exercises use Python with popular RAG frameworks (LangChain, LlamaIndex). Skip them if you’re focused on concepts.

What to expect: 8 lessons, approximately 2.5 hours total. Each lesson includes a quiz, practical exercises, and links to related skill templates.

Key Takeaways

• LLMs have three critical problems: hallucination, knowledge cutoffs, and no domain knowledge
• RAG solves all three by retrieving relevant documents and grounding the LLM’s response in actual data
• RAG has three stages: indexing (prepare documents), retrieval (find relevant chunks), and generation (answer with context)
• RAG is the most practical approach for most use cases — faster than fine-tuning, more scalable than long context, and provides built-in citation

Up Next

In the next lesson, you’ll explore the full RAG architecture in detail — how each of the three stages works and how they connect into a complete system.