From tokenization to production-ready RAG systems. Understand the theory and build the stack.
Before building, we must understand the core mechanics of how LLMs process information.
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LLMs don't read words; they read "tokens" (parts of words).
Example: "unbelievable" → ["un", "believ", "able"].
Approximately 1000 tokens ≈ 750 words.
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A parameter (0.0 - 2.0) that controls creativity.
Low (0-0.3): Factual, deterministic.
High (0.7+): Creative, diverse, risk of hallucination.
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The limit on how much text (input + output) the model can process at once.
Modern models range from 8k to 1M+ tokens.
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Training on trillions of tokens to predict the "next token".
Creates the base model. Distinct from "Fine-tuning" which specializes the model later.
Retrieval-Augmented Generation (RAG) connects LLMs to your private data. Click the components to understand the flow.
Prompt + Context = Answer
Click on any step in the flowchart to learn how RAG works under the hood.
LLMs cannot read your entire database at once. We must split documents (PDFs, Wikis, etc.) into small chunks (e.g., 500 tokens).
We convert text chunks into Embeddings—lists of numbers representing meaning (e.g., [0.1, -0.5, 0.9]). These are stored in a Vector DB (Pinecone, Weaviate, Chroma).
Why? This allows semantic search. "Pet" and "Dog" will have mathematically similar vectors.
When a user asks a question, we convert it to a vector and find the "nearest neighbors" in the database.
We construct a final prompt that includes the retrieved text as "truth".
System: You are a helpful assistant. Use ONLY the provided context.
Context: "Employees can carry over 5 days..."
User: "Can I save my vacation days?"
Modern LLMs perform best when roles are clearly defined using the System, User, and Assistant paradigm.
Zero-shot: Asking directly ("Translate this").
Few-shot: Providing examples first ("Dog:Perro, Cat:Gato, Fish:?"). drastically improves accuracy.
Instead of "What is the answer?", ask "Think step-by-step". This forces the model to generate reasoning tokens before the final answer, reducing math/logic errors.
Test your understanding of the concepts covered in this tutorial.
1. Which of the following best describes "Tokenization"?
2. Why would you use Retrieval-Augmented Generation (RAG)?
3. If you want a model to be very factual and consistent, what Temperature setting should you use?
4. In the context of Agents, what is the "ReAct" pattern?
5. What is the main purpose of the "System Prompt"?