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Lesson 01 – Introduction to Large Language Models

Large Language Models (LLMs) are neural networks trained on massive text datasets to generate and interpret natural language. They power tools like ChatGPT, GitHub Copilot, and many AI writing assistants.

This lesson explains what LLMs are, what they can do, where they fall short, and how to start working with them responsibly.

1. What is a Large Language Model?

At their core, LLMs are predictive engines: they generate text by guessing the next word based on the words that came before.

With billions of examples from books, websites, and conversations, models learn statistical patterns that generalize to a wide range of language tasks—even ones they weren’t explicitly trained for.

Most modern LLMs are built on the transformer architecture, which enables them to:

  • Weigh the relevance of different words in a sentence (self-attention),
  • Capture long-range context (important for paragraphs, not just sentences),
  • Work in parallel for efficient training and inference.

For a visual overview, see the Transformer Architecture diagram.

Importantly, LLMs don’t understand language the way humans do. They simulate fluency based on probability, not comprehension.

graph LR
A[User Prompt] --> B[LLM]
B --> C[Generated Output]

2. What Can LLMs Do?

LLMs support a wide range of practical tasks:

  • Question Answering Ask direct questions and get fluent, often helpful replies.

“What causes seasons?” → "The tilt of Earth's axis causes seasonal changes..."

  • Summarization Distill long content into key points or concise overviews.

  • Code Generation and Help Write boilerplate, explain functions, or debug step-by-step.

  • Text Translation Translate between languages with surprising fluency—though not always perfect.

Other tasks include rewriting, brainstorming, extracting data, generating creative content, and more.

3. Limitations and Ethical Considerations

While powerful, LLMs come with constraints. Use them with awareness.

  • Hallucinations LLMs may fabricate facts or cite nonexistent sources. Always verify critical output.

  • Bias and Fairness Models reflect the data they’re trained on—which may encode social, cultural, or historical biases. Be alert to harmful stereotypes or exclusionary phrasing.

  • Privacy and Safety Don’t submit private data or confidential code when using third-party APIs. Model inputs are not inherently private.

  • Overreliance LLMs can speed up your thinking—but shouldn’t replace it. Treat output as a draft, not ground truth.

4. Example: Using an LLM via API

Here’s a simple Python script that sends a question to an OpenAI model and prints the response.

import openai

openai.api_key = "YOUR_API_KEY"

response = openai.ChatCompletion.create(
    model="gpt-3.5-turbo",
    messages=[{"role": "user", "content": "Explain the Turing test."}]
)

print(response.choices[0].message.content)

📌 This demonstrates how structured prompts power LLM interactions.

5. Practice Prompt (Optional)

Try asking the model something exploratory:

“Outline a plan to teach machine learning to high school students.”

Examine the structure of the reply—what seems helpful? What needs refining?

Screencast

🎥 LLM Demo: Outlining a Project Plan Watch a quick demo of how an LLM can rapidly generate an initial roadmap: LLM Demo Video

What’s Next

In the next lesson, we’ll cover how to write effective prompts that guide the model toward useful, reliable output. Prompt design is both a science and an art—and it's key to unlocking the full potential of LLMs.

By understanding how LLMs generate and interpret text, you've set a solid foundation for AI-assisted development. Building on Module 01's Lesson 10 – CSV Import/Export, which emphasized that clean data exchange enables reliable automation, you're now prepared for Lesson 02 – Prompt Engineering to learn how to craft inputs that steer model behavior.