LLMs

Large Language Models and the GPT architecture.

Not started3 min explanation

Visualize, practice, and deep-dive material are optional—use only what helps you learn.

Explanation

A focused 3-minute explanation using the topic's authored material.

Learning goals and prerequisites

After this lesson

Explain the full LLM lifecycle
Adapt and evaluate a model for a task
Architect a governed LLM product

Helpful before starting

Transformers and autoregressive modeling
Probability, optimization, and evaluation basics
Basic distributed systems and APIs

Start here

LLMs, in plain language

Large Language Models and the GPT architecture. LLMs combine representation learning, language modeling, alignment, retrieval, and high-performance serving into a general-purpose AI platform.

For a small example, after "peanut butter and", several next tokens are possible. Score candidates, normalize with softmax, sample or choose one, append it, and repeat with the expanded context. This is the mechanism to keep in view as the lesson becomes more technical. Before moving on, identify the input, transformation, output, and one observation that would falsify your conclusion.

Key points

Tokenization, embeddings, next-token prediction, context, and decoding.
Pretraining, instruction tuning, preference alignment, and in-context learning.
Prompt roles, sampling parameters, structured outputs, and basic limitations.

Use it well

When LLMs helps—and where it breaks

Employees ask questions over internal policies. Define tasks, compare prompting with retrieval, create evaluation cases, constrain outputs, and inspect unsupported answers. A useful result still depends on checking the assumptions and evidence below rather than treating one successful output as proof.

Key points

Using a larger model before defining the task. Better approach: Create a representative evaluation set and baseline first.
Treating fluency as correctness. Better approach: Require grounded evidence or executable verification where possible.
Logging sensitive prompts indiscriminately. Better approach: Apply minimization, redaction, retention, and access controls.

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