Chapter 1: Introduction to Generative AI

Chapter 1: Introduction to Generative AI#

“Generative models are a key enabler of machine creativity, allowing machines to go beyond what they’ve seen before and create something new.” — Ian Goodfellow

Why it matters#

Most machine learning you have likely met before is discriminative: it takes an input and predicts a label, such as “spam or not spam” or “cat or dog.” Generative AI does something qualitatively different. It produces new content, text, images, audio, code, that resembles its training data but did not exist before. This shift from predicting to creating is what makes the technology feel new, and it is why a single model can now draft an email, summarize a contract, answer a question, and write code.

This chapter sets up the whole book. It introduces the two ideas that everything else rests on, foundation models and large language models, surveys what people actually use these models for, and shows how Amazon Bedrock turns them into something you can call from an application with a few lines of code.

Foundation models#

A foundation model is a large machine learning model that is pre-trained on vast amounts of data and can then be adapted to many more specialized tasks. The word “foundation” is deliberate: the model is a base you build on rather than a finished solution for one narrow problem.

Two properties make foundation models distinctive:

  • They are pre-trained on huge, broad datasets. Rather than learning one task from a small curated dataset, they absorb general structure from data at web scale.

  • They can be trained on any kind of data. Text, images, video, and audio can all serve as training signal, which is why the same family of techniques powers chatbots, image generators, and speech systems.

Definition

A foundation model is a large model pre-trained on broad data that can be adapted (through prompting or fine-tuning) to a wide range of downstream tasks.

Large language models#

A large language model (LLM) is a foundation model trained on text. At its core, an LLM is a very large statistical model that learns the probabilities of words appearing in particular contexts. Its training task is deceptively simple: predict a missing or next word in a sequence.

Consider the sentence:

“The weather has been cloudy for the last two days. Most likely it will be ____ tomorrow.”

To fill the blank well (cloudy? sunny? foggy?), the model must pick up grammar, facts, and a little common-sense reasoning, all from the single objective of predicting text. Scaled up over enormous datasets, this next-word objective is enough to produce models that write fluently, answer questions, and follow instructions.

How big are these models?#

State-of-the-art models are genuinely enormous. To make the scale concrete, the largest models are comparable in size to:

  • a 474-million-page document,

  • 35 hours of 4K video, or

  • a codebase with 80 billion lines of code.

That scale has a cost. Training a frontier model can require hundreds of people and exceed 100 million dollars in compute. We return to these costs, and their environmental impact, in Chapter 2: Foundation Models and Large Language Models.

What LLMs are used for#

LLMs are not a single product; they are a general capability that shows up across many domains, education, healthcare, customer service, marketing, finance, and law among them. The most common application patterns are worth naming because the rest of the book keeps coming back to them.

Conversational chatbots. Interactive applications that hold human-like, context-aware dialogue, remember earlier turns, and answer follow-up questions. These power virtual assistants and support agents.

Interactive training and education. Rapid generation of personalized, multilingual learning content, slides, exercises, quizzes, and tailored explanations for a specific audience.

Creative assistants. Prompt-based generation of written content, art, and music, where the user steers the output with natural-language instructions and sometimes images or audio.

Productivity tools. Automating routine work: drafting and summarizing documents, generating and commenting code, writing test cases, and drafting or auto-completing email.

Data analytics. Surfacing hidden patterns in data (sentiment, topics, personally identifiable information), interpreting charts, generating reports, and creating synthetic data for testing.

Worked example: from task to pattern

Suppose a retailer wants to (a) answer customer questions, (b) summarize product reviews, and © write product descriptions. Rather than building three separate ML systems, all three map onto one LLM through different prompts: a chatbot prompt, a summarization prompt, and a text-generation prompt. Recognizing which pattern a business problem fits is the first design skill in generative AI.

Amazon Bedrock#

Knowing what LLMs can do raises a practical question: how do you actually use one without standing up a cluster of GPUs? This is the gap Amazon Bedrock fills.

Amazon Bedrock is a fully managed service that makes foundation models available through a single API. Instead of hosting models yourself, you call an endpoint and pay for what you use. Bedrock offers models from several providers behind a consistent interface, including:

Provider

Example model family

Amazon

Titan

AI21 Labs

Jurassic-2

Anthropic

Claude

Cohere

Command

Meta

Llama

What makes Bedrock attractive for real applications is not just convenience but governance:

  • You can privately customize foundation models with your own data.

  • You can integrate models into applications using familiar AWS tools without provisioning or managing infrastructure.

  • Your prompts and responses are not shared with AWS or third-party model providers.

  • Bedrock adds security capabilities such as encryption, identity and access management (IAM), and a range of compliance designations.

Amazon Titan models#

Amazon’s own family on Bedrock is Titan. Titan is positioned around responsible, high-performing models and comes in two flavors that you will use repeatedly:

  • Titan Text is a generative model for summarization, text generation (for example, drafting a blog post), classification, open-ended question answering, and information extraction.

  • Titan Embeddings translates text into numerical vectors, embeddings, that capture the semantic meaning of the text. Embeddings are the backbone of search and personalization, and they reappear in Chapter 5: Multimodal Prompting and in Module 3’s chapter on retrieval-augmented generation.

Common Bedrock use cases#

The service documentation groups Bedrock use cases into a handful of recurring shapes, which line up neatly with the LLM applications above:

  • Text generation: create original content such as stories, posts, and pages.

  • Chatbots: build conversational assistants.

  • Search: find and synthesize answers from a large corpus.

  • Text summarization: condense articles, books, and documents.

  • Image generation: create images from language prompts.

  • Personalization: deliver more relevant, contextual recommendations than simple word matching.

In the news#

Generative AI moved from research demos to mainstream tooling remarkably fast. Amazon Bedrock became generally available in 2023 and has steadily expanded the catalog of models it offers, adding newer Anthropic Claude versions, Meta Llama models, and Amazon’s own Titan and later Nova models. Two broader trends frame this chapter:

  • Managed access is the norm. The industry has converged on consuming foundation models as managed API services rather than self-hosting, which is exactly the pattern Bedrock embodies.

  • Capability is generalizing. The same underlying models increasingly handle text, images, and code together, foreshadowing the multimodal chapter at the end of this module.

Because this field changes monthly, always check the Amazon Bedrock documentation for the current list of available models and features.

Hands-on labs#

The labs for Module 1 begin with Amazon Bedrock. In the console-based Lab 1 (see the source repository) you make predictions with foundation models directly, and in Lab 2a: Introduction to Amazon Bedrock you call Bedrock programmatically with Boto3. Read this chapter first, then open the labs from Module 1 Labs: Hands-on with Amazon Bedrock.

Key takeaways#

  • Generative AI creates new content rather than only classifying inputs.

  • A foundation model is pre-trained on broad data and adapted to many tasks; an LLM is a foundation model trained on text whose core skill is predicting the next word.

  • Frontier models are enormous and expensive to train, which is why most teams consume them as a service.

  • Amazon Bedrock provides foundation models from multiple providers through one secure, managed API, with Amazon Titan as Amazon’s own text and embedding models.

In the next chapter we open up the model itself: what foundation models are made of, how the transformer architecture works, and where LLMs fall short.