LLMs · Guide
Large Language Models (LLMs): A Complete Guide
How LLMs work — tokens, attention, context windows, prompting, function calling, fine-tuning.
A large language model (LLM) predicts the next token from patterns it learned across huge text corpora. This guide covers how LLMs work, tokenization and context windows, transformer attention, prompt engineering, function calling and structured output, and how to choose between fine-tuning, RAG, and prompting.
Generate your own lesson →What you'll learn
- Prompt Engineering Foundations
- Structured Outputs with JSON Schema
- Tokenization and Context Windows
- Fine-tuning vs RAG vs Prompting
- Pretraining and Scaling Laws
- Function Calling and Tool Use
- Quantization, Distillation, and Inference Optimization
- RLHF, DPO, and Preference Tuning
- Transformer Attention Explained
- How Large Language Models Work
Lessons in this guide (10)
Prompt Engineering Foundations
Design prompts as testable interfaces, not magic wording.
Structured Outputs with JSON Schema
Make model responses parseable, validated, and app-safe.
Tokenization and Context Windows
See how text becomes tokens and why context is a scarce design budget.
Fine-tuning vs RAG vs Prompting
Choose the right adaptation lever for instructions, knowledge, or behavior.
Pretraining and Scaling Laws
Understand why data, parameters, and compute changed model capability.
Function Calling and Tool Use
Connect models to APIs through typed tool calls and controlled execution.
Quantization, Distillation, and Inference Optimization
Shrink latency and cost without losing the behavior your product needs.
RLHF, DPO, and Preference Tuning
Understand how feedback and preferences steer model behavior after pretraining.
Transformer Attention Explained
Learn queries, keys, values, heads, and why attention scaled language models.
How Large Language Models Work
Trace token prediction, context, sampling, and why fluent text can still be wrong.
Frequently asked questions
How do LLMs work?
An LLM is a transformer neural network trained to predict the next token. Given your prompt, it repeatedly predicts the most likely next token, building the response one token at a time from patterns learned in training.
What is a context window?
The context window is the maximum amount of text (measured in tokens) a model can consider at once — your prompt plus its response. Everything the model 'sees' for a request must fit inside it.
What is prompt engineering?
Prompt engineering is structuring your instructions, examples, and context so the model reliably produces the output you want — via clear tasks, few-shot examples, output format, and constraints.