Hands-On Large Language Models

Large language models feel like magic until someone explains them without the jargon. That’s what this book does exceptionally well.

If you’ve used ChatGPT or Claude and wondered “how does it actually work?”—but didn’t want to wade through research papers—this book is the bridge. Jay Alammar (known for his visual explanations of ML concepts) and Maarten Grootendorst make LLMs comprehensible without dumbing them down.

What I appreciated most

The book doesn’t assume you’ve trained models before. It starts from “what is a token?” and builds up to transformers, embeddings, and vector databases in a way that actually sticks.

Here’s the mental model it gave me:

Tokens are the atomic unit of language for an LLM. When you type “Hello, world!” the model doesn’t see letters or words—it sees tokens. A token might be a word, part of a word, or even punctuation. The tokenizer breaks your input into these chunks, and each token gets mapped to a number (an ID).

So “Hello, world!” might become something like [15496, 11, 995, 0] (not the actual IDs, but you get the idea).

The model works entirely in numbers. It takes those token IDs, converts them into vectors (lists of numbers called embeddings), does a bunch of math (the transformer part), and outputs probabilities for what token should come next.

Then the process reverses: the model picks a token ID, and the tokenizer converts it back into text you can read.

Input text → tokenizer → token IDs → embeddings → transformer → output probabilities → token ID → detokenizer → output text.

That’s the flow. Everything in between—the “magic”—is just math on vectors.

Embeddings and vector databases (the practical part)

One of the most useful sections covers embeddings and why they matter beyond just running the model.

An embedding is a vector representation of meaning. The word “king” might be [0.2, 0.8, 0.1, ...] (in reality, hundreds or thousands of dimensions). The word “queen” would have a similar but slightly different vector.

The key insight: similar meanings = similar vectors. You can measure “closeness” using math (cosine similarity, dot product). This is how semantic search works.

If you want to build something like “search my documents by meaning, not just keywords,” you:

1. Convert each document chunk into an embedding
2. Store those embeddings in a vector database
3. When a user asks a question, convert the question into an embedding
4. Find the closest document embeddings
5. Feed those documents to the LLM as context

This pattern—Retrieval-Augmented Generation (RAG)—is how you give an LLM access to knowledge it wasn’t trained on. The book walks through this with real examples, not just theory.

Transformers explained for a 15-year-old

The transformer architecture is what makes modern LLMs work. Here’s the simplest way to think about it:

Attention is about focusing on what matters.

Imagine you’re reading this sentence: “The cat sat on the mat because it was tired.”

When you read “it,” your brain automatically knows “it” refers to “the cat,” not “the mat.” You paid attention to the right word.

Transformers do the same thing, but with math. When the model processes “it,” the attention mechanism looks back at all the previous tokens and figures out which ones are relevant. It learns that “it” should pay attention to “cat” more than “mat.”

That’s the core idea. The model has multiple “attention heads” that each focus on different patterns (grammar, meaning, relationships). It does this in layers, building up more complex understanding as it goes deeper.

Positional encoding is the trick that tells the model where each token is in the sequence. Without it, “cat sat on mat” and “mat on sat cat” would look identical to the model.

The book uses diagrams (Jay’s specialty) to show how attention flows between tokens. If you’re a visual learner, this is where the book really shines.

Why this book works

Most ML books either:

• assume you already know linear algebra and backpropagation, or
• stay so high-level that you can’t actually build anything

This one finds the middle ground. It explains enough theory that you understand why things work, but focuses on the practical patterns you’ll actually use:

• How to prompt effectively (not just “be nice to the AI”)
• When to fine-tune vs when to use RAG
• How to evaluate if your LLM feature is actually working
• What failure modes to watch for (hallucinations, prompt injection, context limits)

The tone is conversational. The examples are concrete. The code snippets are in Python and actually run.

Who should read this

If you’re a software engineer who wants to build with LLMs—not just call an API and hope—this book will save you weeks of trial and error.

You don’t need to be an ML expert. You don’t need a math degree. You just need to be curious about how these systems work and willing to run some code.

After reading it, you’ll understand:

• What’s happening when you send a prompt to an API
• Why context windows matter and how to work around them
• How embeddings enable semantic search
• When fine-tuning is worth the effort (and when it’s not)
• How to debug when your LLM feature behaves weirdly

Highly recommended if you’re building with LLMs and want to move beyond trial-and-error prompting—or simply want to understand what’s actually happening under the hood.