GraphAcademy

Neo4j & GenerativeAI Fundamentals

Generative AI

Retrieval Augmented Generation (RAG)

Knowledge Graphs

Integrating Neo4j with Generative AI

What is Generative AI

GenAI

Generative AI (or GenAI) refers to artificial intelligence systems designed to create new content that resembles human-made data. The data could be text, images, audio, or code.

These models, like GPT (for text) or DALL-E (for images), are trained on large datasets and use patterns learned from this data to generate new output.

A diagram showing the process of Generative AI

Generative AI is widely used in applications such as chatbots, content creation, image synthesis, and code generation.

Generative AI models are not "intelligent" in the way humans are. They do not understand or comprehend the content they generate. Instead, they rely on statistical patterns and correlations learned from their training data.

While Generative AI models can produce coherent and contextually relevant outputs, they lack understanding.

Large Language Models (LLMs)

This course will focus on text-generating models, specifically Large Language Models (LLMs), which are a type of generative AI model designed to understand and generate human-like text.

These models are trained on vast amounts of text data and can perform various tasks, including answering questions, summarising data, and analysing text.

The response generated by an LLM is a probabilistic continuation of the instructions it receives. The LLM provides the most likely response based on the patterns it has learned from its training data.

In simple terms, if presented with the prompt "Continue this sequence - A B C", an LLM could respond "D E F".

To get an LLM to perform a task, you provide a prompt, a piece of text that should specify your requirements and provide clear instructions on how to respond.

A user asks an LLM the question 'What is an LLM? Give the response using simple language avoiding jargon.'

Precision in the task description, potentially combined with examples or context, ensures that the model understands the intent and produces relevant and accurate outputs.

An example prompt may be a simple question.

What is the capital of Japan?

Or, it could be more descriptive. For example:

You are a friendly travel agent
helping a customer to choose
a holiday destination. Your readers
may have English as a second
language, so use simple terms
and avoid colloquialisms.
Avoid Jargon at all costs.
Tell me about the capital of Japan.

The LLM will interpret these instructions and return a response based on the patterns it has learned from its training data.

Check Your Understanding

Generative AI

Which of the following statements are true about Generative AI? (Select all that apply.)

  • ✓ Generative AI models can generate new content such as text, images, or code.

  • ✓ Large Language Models (LLMs) generate text based on patterns learned from training data.

  • ✓ The quality of a prompt can affect the relevance and accuracy of a model’s response.

  • ❏ Generative AI models always understand the meaning of the content they produce.

Hint

Consider how generative AI models work, what they can create, how they use prompts, and whether they truly understand the content they generate.

Solution

The following statements are true:

  • ✓ Generative AI models can generate new content such as text, images, or code.

  • ✓ Large Language Models (LLMs) generate text based on patterns learned from training data.

  • ✓ The quality of a prompt can affect the relevance and accuracy of a model’s response.

Generative AI models do not understand the meaning of the content they produce.

Lesson Summary

In this lesson, you learned about Generative AI and Large Language Models (LLMs).

In the next lesson, you will learn about the limitations of LLMs, including hallucination, and access to data.