Introduction

This page provides an overview of essential Generative AI concepts, such as intent classification, inference, and AI workflow automation, crucial for understanding and building intelligent AI agents.

What is Generative AI?

Generative AI refers to artificial intelligence systems that can create new content, such as text, images, music, and even code, by learning from patterns in data. Unlike traditional AI models that classify or predict based on existing data, generative AI can generate novel outputs.

How Does It Work?

Generative AI learns by studying patterns in massive datasets and then tries to create something similar. Here’s a simple way to think about it:

1. Learning from Data: The AI looks at millions of examples (e.g., books, paintings, music) and figures out the common patterns. Example: If you show it thousands of cat pictures, it learns what a "cat" looks like.

2. Making Predictions: When given a prompt (like a sentence or an image idea), the AI predicts what should come next based on what it has learned. Example: If you ask an LLM (Large Language Model) ChatGPT to write a poem, it guesses the next best words based on how poems are usually written.

3. Refining the Output: Advanced AI models improve their outputs over time by constantly fine-tuning their results based on feedback. Example: In the lending and loan origination space, an AI model used for credit risk assessment gets better at predicting loan defaults by continuously learning from past loan performance.

   1. Initially, the AI analyzes historical financial spreadsheets, borrower credit scores, and income statements to assess loan risk.

   2. If the model predicts that a borrower is low-risk but they later default, the system adjusts its criteria based on this new information.

   3. Over time, it becomes more accurate at detecting risky applicants and improving loan approval decisions.