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Learn Large Language Models

From tokens and attention to fine-tuning, retrieval and evaluation.

Free to start. Built from a single prompt. No credit card.

A large language model is a neural network trained on huge amounts of text to predict the next token, and that single objective turns out to produce systems that can summarize, translate, write code and answer questions. Understanding one means understanding tokenization, embeddings, the transformer architecture and its attention mechanism, then the training stages that follow pretraining, such as instruction tuning and preference tuning. You also need a clear picture of their weak spots, like hallucination and a fixed context window. Learnr builds a course on LLMs at the depth you choose, from a conceptual tour to the details engineers use, in short interactive lessons.

How to learn Large Language Models, step by step

Start with tokens, not neurons. Paste sentences into a public tokenizer viewer and watch words split into subword pieces. Seeing that a model reads token IDs, not letters, explains odd behavior like miscounting characters in a word and why prices are quoted per token.
Learn embeddings and attention with small examples. Treat an embedding as a list of numbers that places a token in a meaning space. Then work through query, key and value vectors for a three-word sentence by hand, so the attention formula stops being a black box.
Trace how a base model becomes an assistant. Pretraining on next-token prediction produces a model that continues text. Instruction tuning on example conversations and preference tuning such as RLHF shape it into something that follows requests. Knowing these stages explains both helpfulness and refusals.
Experiment with sampling settings. Use an API playground or a small open-weights model to change temperature and top-p on the same prompt. Low temperature gives repeatable answers, higher values add variety and more mistakes, which matters when you build anything on top of a model.
Build a tiny retrieval-augmented app. Split a few documents into chunks, embed them, retrieve the closest chunks for a question and pass them to the model as context. This one project teaches vector search, context limits and why grounding reduces made-up answers.
Practice evaluation and keep concepts fresh. Write a small test set of questions with known answers and score a model against it before trusting any change. Short daily Learnr lessons help you revisit attention, sampling and fine-tuning terms until they feel natural.

What you'll learn

Explain how text becomes tokens and embeddings inside a model
Describe self-attention and why transformers replaced recurrent networks for language
Distinguish pretraining, instruction tuning and preference tuning
Choose between prompting, retrieval-augmented generation and fine-tuning for a task
Evaluate model output for hallucinations, bias and failure cases

What's inside your Large Language Models course

A sample path Learnr builds for Large Language Models. Your real course adapts to your goal and level.

1. Tokens, vocabularies and embeddings
2. The transformer and self-attention
3. Pretraining on next-token prediction
4. Instruction tuning, RLHF and alignment
5. Context windows, sampling and temperature
6. RAG, fine-tuning and evaluating LLM systems

How long does it take to learn Large Language Models?

A solid conceptual grasp of how LLMs are built and where they fail takes about 3 to 5 weeks at 3 to 4 hours a week. Building and evaluating LLM applications in code usually takes 2 to 4 months more, faster if you already write Python and know basic machine learning.

What you need before you start

For the conceptual path, nothing beyond curiosity. To build with LLMs, basic Python and comfort calling a web API help, and some linear algebra makes attention and embeddings far easier to follow.

Common mistakes to avoid

Assuming the model looks facts up. Unless it is connected to search or retrieval, it generates likely text from patterns, so verify specific claims.
Jumping to fine-tuning when better prompting or retrieval would solve the problem more cheaply and with less data.
Changing prompts or models without a fixed set of test questions, which makes it impossible to tell whether quality improved or got worse.

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Large Language Models questions, answered

Do I need to know deep learning to understand LLMs?

Not for a working understanding. You can learn tokens, attention, training stages and limitations conceptually. If you want to train or modify models, study neural networks and backpropagation first.

What is the difference between an LLM and a chatbot?

The LLM is the underlying model that predicts text. A chatbot is a product built around one or more models, adding a chat interface, system instructions, safety filters and often tools such as search or code execution.

Why do language models hallucinate?

They are trained to produce plausible continuations, not to check truth. When the training data is thin or the question is ambiguous, a fluent but wrong answer can still be the most likely text, so grounding and verification matter.