Learning Objectives — Large Language Models & Embeddings

By the end of this lesson, learners will be able to:

• Define what a Large Language Model (LLM) is.
• Explain how LLMs process and generate text.
• Understand the role of tokens in language models.
• Describe what a context window is and why it matters.
• Define embeddings and explain their purpose.
• Understand how semantic search works using embeddings.
• Explain the relationship between prompts and model responses.
• Recognize common use cases for LLMs and embeddings.
• Identify prompt injection as a key AI security risk.
• Apply LLM concepts to real-world AI systems and certification exam scenarios.

Key Concepts — Large Language Models & Embeddings

• Large Language Models (LLMs)
• Natural Language Processing (NLP)
• Tokens
• Tokenization
• Context Window
• Transformer Architecture
• Foundation Models
• Generative AI
• Next-Token Prediction
• Parameters
• Prompt
• Response
• Prompt Engineering
• Embeddings
• Vector Representations
• Semantic Meaning
• Semantic Search
• Similarity Matching
• Vector Databases
• Cosine Similarity
• Information Retrieval
• Recommendation Systems
• Prompt Injection
• AI Security
• AI Applications

Transcript — Large Language Models & Embeddings

Welcome to Lesson 1.5: Large Language Models and Embeddings.

Large Language Models, often called LLMs, are one of the most transformative technologies in modern Artificial Intelligence.

They power many of the AI applications people interact with every day, including chatbots, virtual assistants, content generation tools, coding assistants, search systems, and language translation platforms.

In this lesson, we’ll explore how Large Language Models work at a conceptual level, how they process text, what tokens and context windows are, why embeddings are important, and how these technologies enable applications such as semantic search.

We’ll also discuss prompting and an important security concern known as prompt injection.

Let’s begin with Large Language Models.

A Large Language Model is a type of AI model trained on enormous collections of text.

These datasets often include books, websites, articles, documentation, research papers, and source code.

The goal is to learn patterns within language so the model can generate useful responses when given new input.

One of the most important concepts to understand is that Large Language Models do not think like humans.

Instead, they predict the most likely next piece of text based on patterns learned during training.

This process is known as next-token prediction.

To understand next-token prediction, we first need to understand tokens.

A token is a small unit of text.

Depending on the language model, a token may represent a word, part of a word, punctuation mark, or group of characters.

For example, a sentence is not processed as one large block of text.

Instead, it is broken into many smaller tokens.

The model analyzes these tokens and predicts which token is most likely to come next.

This process happens repeatedly, one token at a time.

The result is a complete response that appears natural and conversational.

This simple idea of predicting the next token is the foundation of modern language generation.

As language models grow larger and are trained on more data, their ability to generate coherent and useful responses improves significantly.

Modern LLMs often contain billions or even trillions of parameters.

Parameters are the internal values learned during training that help the model recognize patterns and relationships within language.

Generally speaking, larger models can capture more complex patterns and demonstrate stronger performance across a wider range of tasks.

Another critical concept is the context window.

The context window represents the amount of information a model can consider at one time.

Think of it as the model’s short-term memory.

When you interact with an AI chatbot, the model can only use information that fits within its context window.

Everything inside the window is available to the model.

Anything outside the window is effectively forgotten.

Early language models had relatively small context windows.

Modern systems can process hundreds of thousands or even millions of tokens.

Larger context windows allow models to analyze lengthy documents, maintain longer conversations, and process larger amounts of information simultaneously.

However, context windows are not unlimited.

Every model has a maximum capacity.

This is an important concept because if a conversation or document exceeds the available context window, earlier information may be removed from consideration.

For certification exams, remember that the context window defines the maximum amount of information a model can use at one time.

Now let’s move to embeddings.

Embeddings are one of the most important concepts in modern AI systems.

An embedding is a numerical representation of data.

More specifically, an embedding is a vector—a list of numbers—that captures meaning and relationships.

Computers cannot directly understand words or concepts.

They work with numbers.

Embeddings provide a way to convert language, images, audio, and other information into numerical formats that preserve meaning.

The power of embeddings comes from similarity.

Concepts with similar meanings tend to have embeddings that are located close together within vector space.

For example, the words “doctor” and “physician” have similar meanings.

As a result, their embeddings are likely to be positioned near each other.

Words with unrelated meanings tend to be farther apart.

This allows AI systems to measure similarity mathematically.

Embeddings enable many advanced AI capabilities.

One of the most important applications is semantic search.

Traditional keyword search looks for exact word matches.

If you search for the word “physician,” a traditional search engine may only return documents containing that specific word.

Semantic search works differently.

The query is converted into an embedding.

Documents are also converted into embeddings.

The system compares these vectors and identifies information with similar meaning.

As a result, a search for “physician” may also return documents containing words like “doctor” or “medical professional.”

This creates a much more intelligent search experience.

Semantic search is widely used in enterprise search systems, recommendation platforms, retrieval-augmented generation systems, and AI-powered knowledge bases.

Another important concept is prompting.

A prompt is the instruction or input provided to a language model.

Everything a user types into a chatbot is a prompt.

The output generated by the model is called the response.

The quality of the response often depends heavily on the quality of the prompt.

For example, asking a model to “summarize this report” may produce a generic answer.

Asking it to “summarize this report in five bullet points for a business executive” provides much clearer guidance.

This often results in more useful output.

The practice of designing effective prompts is known as prompt engineering.

Prompt engineering helps users guide AI systems toward desired outcomes.

As organizations adopt AI, prompt engineering has become an increasingly valuable skill.

However, prompting also introduces security concerns.

One of the most important is prompt injection.

Prompt injection occurs when malicious instructions attempt to manipulate a model’s behavior.

An attacker may try to override existing instructions or trick the system into revealing information it should not disclose.

For example, a malicious prompt might instruct a system to ignore its previous rules or reveal restricted content.

Prompt injection is often compared to SQL injection in traditional cybersecurity because both involve manipulating system behavior through crafted input.

Organizations mitigate prompt injection risks using techniques such as input validation, content filtering, guardrails, monitoring, and human oversight.

As AI systems become more widely deployed, securing prompts and model interactions becomes increasingly important.

For certification exams, there are several key concepts to remember.

Large Language Models generate text through next-token prediction.

Tokens are the small units of text processed by the model.

The context window defines how much information the model can consider at one time.

Embeddings are vector representations that capture semantic meaning.

Semantic search uses embeddings to find information based on meaning rather than exact keywords.

Prompts are user inputs.

Responses are model outputs.

Prompt injection is a significant AI security risk.

To summarize:

Large Language Models are AI systems trained on massive amounts of text.

They generate content by predicting the next token in a sequence.

Tokens are the building blocks of language processing.

Context windows determine how much information a model can use at once.

Embeddings convert information into numerical vectors that capture meaning.

Semantic search uses embeddings to retrieve information based on similarity and context.

Prompt engineering helps users improve AI responses.

Prompt injection represents an important security challenge that organizations must address.

Together, these concepts form the foundation of modern Generative AI systems and help explain how today’s most advanced AI applications operate.