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Introduction to Large Language Models

Relax. We'll talk through this in plain words — no textbook voice.

What you'll walk away with

  • Understand Introduction to Large Language Models without the intimidation factor
  • Get hands-on practice trying it yourself
  • Spot the common pitfalls and sidestep them with a smile

Learn how an LLM receives, predicts, and generates text — and why you shouldn't think of a model as a database.

Let's think about it this way for a moment

A Large Language Model isn't a database that copies an answer out of some giant book. It's a model that has learned the probability of the next token from huge amounts of text patterns. That's why it can write beautifully and still get facts wrong.

Introduction to Large Language Models lesson illustration
Prompting and LLMs — Introduction to Large Language Models

Let's connect this to everyday life

You can use an LLM as a reasoning assistant, a summarizer, a classifier, and a content drafting tool. For anything the model couldn't possibly know — like recent events or your company's internal data — you need to supply it through context, retrieval, or a tool.

Let's try it hands-on together

text
User message + Instructions + Context
                 ↓
          Language Model
                 ↓
       Probable next tokens
                 ↓
              Response
You should see
You'll be able to explain an LLM's strengths and limitations to someone else.

5-minute try-it

Pick a feature you'd build using an LLM, then write three columns: what the model can know, what it can't know, and what context you'd need to supply.

A quick word of caution

Don't treat AI output as the final, guaranteed-correct answer. For anything important — critical facts, code, or user data — have a human review it before you use it.

OpenAI — Text generation — OpenAI

Easy traps

  • Treating an LLM like a search engine that's always right
  • Assuming that as a model gets bigger, you no longer need domain-specific data

Exercise

Pick a feature you'd build using an LLM, then write three columns: what the model can know, what it can't know, and what context you'd need to supply.

You'll know it worked when: You'll be able to explain an LLM's strengths and limitations to someone else.

Introduction to Large Language Models | Thuta Learning