LLMs and how they work
Words made simple
A few useful meanings before you read the explanation.
- LLM
- A model trained to work with language. It can write or summarize text, but its answer still needs checking against reliable information.
- Neural network
- A model made of connected numerical processing units. Training adjusts its numbers so it can learn patterns from examples.
- Token
- A small piece of text processed by a model. It may be a whole word, part of a word, or punctuation.
More words from this lesson
- Inference
- Using a model that has already been trained to produce an answer or prediction.
- Attention
- A model mechanism that combines information from different parts of the input when deciding what to produce next.
- Grounding
- Connecting an answer to supplied evidence, such as a policy document or a verified database result.
- Hallucination
- An answer that sounds convincing but contains invented or unsupported information.
Overview
- A large language model, or LLM, is a model trained on large amounts of data to process and generate language.
- It learns patterns in how text is arranged and can use those patterns for summarization, extraction, explanation, and tool selection.
- It does not automatically know your database or verify the truth of each sentence.
Why this matters
It provides a reusable language interface without training a separate model for every wording or task.
An everyday example
Imagine an extremely practiced writing assistant who can continue many kinds of text but needs your company records to answer company-specific questions.
A technical example
generate("Summarize this accident description in three bullets: ...") returns a useful draft.
How it works
- Text is split into tokens
- Converted into numerical representations
- Processed through neural-network layers
- Scores are produced for possible next tokens
- Tokens are selected repeatedly until completion.
- Attention is a mechanism that lets the model combine information from different positions in its input; training adjusts the numerical parameters used by that process.
Architecture and flow
Prompt → tokenizer → model inference → generated tokens → decoded text. Inference means using an already trained model.
The insurance assistant example
The assistant rewrites backend claim status awaiting_documents into “We are waiting for your repair invoice.” It must get that status from a tool first.
When to use it
For unstructured language transformation, grounded explanations, or bounded selection between tools.
When to avoid it
As the source of truth for claim status, coverage eligibility, prices, or access permissions.
Interview answer
“An LLM generates language from learned patterns and supplied context. I use it for interpretation and communication, while tools and business services provide verified facts and actions.”
Benefits, trade-offs, and production considerations
- A hallucination is output that is unsupported or false even though it sounds convincing.
- Better prompting reduces some errors but cannot turn a probabilistic model into a trustworthy database.
- Version prompts and models and evaluate each change.
Can you explain it without the notes?
LLMs and how they work
Use your own words. Explain an example and one limitation before checking the explanation.
Check your explanation
A large language model, or LLM, is a model trained on large amounts of data to process and generate language. It learns patterns in how text is arranged and can use those patterns for summarization, extraction, explanation, and tool selection. It does not…
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Practice this conceptAdapted from 13_AI.MD · “LLMs and how they work”. Examples are study fragments; framework behavior can depend on your version.