---
title: 'Introduction to Generative AI'
source: 'https://youtube.com/watch?v=2p5OHDxR2l8'
video_id: '2p5OHDxR2l8'
date: 2026-09-03
duration_sec: 497
channel: 'ByteByteGo'
---

# Introduction to Generative AI

> Source: [Introduction to Generative AI](https://youtube.com/watch?v=2p5OHDxR2l8)

## Summary

This video provides a foundational overview of Generative AI (GenAI), covering key terminology, how to use model APIs, building AI applications, and techniques like RAG and fine-tuning. It is designed for beginners looking to understand and start working with GenAI technologies.

### Key Points

- **Introduction to GenAI** [00:00] — The video introduces Generative AI (GenAI) and outlines the agenda: understanding key terminologies, using model APIs, building AI applications, and customizing models.
- **AI and Machine Learning basics** [00:17] — AI is defined as tasks requiring human intelligence. Machine Learning is a subset of AI where models learn from data without explicit programming.
- **Deep Learning and NLP** [00:51] — Deep learning uses artificial neural networks and is a subfield of ML. NLP focuses on enabling computers to understand and generate human language.
- **Transformer models** [01:19] — Transformers, introduced in the 2017 paper 'Attention is All You Need', rely on self-attention and are the foundation for models like BERT, GPT, and T5.
- **Generative AI defined** [01:47] — GenAI refers to AI systems that generate new content (text, images, music) by learning patterns from existing data using deep learning.
- **Large Language Models (LLMs)** [02:15] — LLMs are trained on vast text data and can perform tasks like answering questions, writing essays, and coding. They are central to many GenAI applications.
- **Prompt engineering** [02:46] — Prompt engineering is the practice of designing effective prompts to get desired outputs, understanding model capabilities, limitations, and biases.
- **Using Model APIs** [03:43] — To use GenAI models, obtain API access, authenticate with an API key, and follow best practices like optimizing parameters and respecting rate limits.
- **Building AI applications** [04:45] — Use cases include marketing, customer support, finance, and education. Example: building a chatbot for personalized book recommendations using an LLM.
- **RAG and Fine-tuning** [06:03] — RAG gives the model access to external data sources in real-time. Fine-tuning adapts a pre-trained model to specific domains or tasks.
- **Conclusion and resources** [07:46] — GenAI offers many possibilities. The video promotes a system design newsletter at blog.bytebytego.com.

## Transcript

Today, we’re diving into the exciting  world of Generative AI, or GenAI for short. With new models and applications emerging  daily, it's important to stay ahead. key terminologies, using Model  APIs, building AI applications,
Let's dive in and unlock the  potential of GenAI together. Let's start with the first step:  understanding the GenAI terminologies. tasks that typically require human intelligence.
It includes various subfields,  such as Machine Learning,   Machine Learning is a subset of AI and improve from data without  being explicitly programmed.
It involves training models on data to recognize  patterns, make predictions, or take actions. which uses artificial neural networks  and is a subfield of Machine Learning. NLP, or Natural Language Processing,  is a subfield of AI that focuses  
on enabling computers to understand,  interpret, and generate human language. sentiment analysis, machine  translation, and text generation.
Deep learning models, particularly Transformer  models, have revolutionized NLP in recent years. Speaking of Transformer models,  they're a type of deep learning   model architecture introduced in the famous  paper "Attention is All You Need" in 2017.
They rely on self-attention mechanisms to process  and generate sequential data, such as text. Transformers have become the foundation for  many NLP models, such as BERT, GPT, and T5.
They've also been adapted for other domains,  like computer vision and audio processing. GenAI, short for Generative  Artificial Intelligence,   refers to AI systems that can generate new  content, such as text, images, or music.
GenAI models can generate novel outputs  that resemble the training data. They use   deep learning models to learn patterns  and representations from existing data.
An important concept in GenAI is  the Large Language Model, or LLM. LLMs are a type of AI model  trained on vast amounts of   These models can perform a wide range of language  
tasks, from answering questions  to writing essays and even coding. LLMs are at the heart of many GenAI  applications, which we'll discuss later. This is the practice of designing effective  prompts to get desired outputs from GenAI  
models. It involves understanding the model's  capabilities, limitations, and biases. relevant examples, and context  to guide the model's output.
Prompt engineering is a critical skill  for getting the most out of GenAI models. Now that we've covered the terminologies,  let's move on to using the Model APIs.
To get started, you'll need to obtain  API access from the desired platform,   Once you have your API key, you can authenticate  your requests to the GenAI model endpoints.
Authentication usually involves providing the  API key in the request headers or as a parameter. It's very important to keep your API key  secure and avoid sharing it publicly. it's important to follow best practices  to ensure reliability and efficiency.
Optimize API usage by carefully  selecting the model parameters,   This is necessary to balance the  desired output quality with costs. When making API requests, be mindful of  the rate limits imposed by the platform.
Exceeding the rate limits may result in API  errors or temporary access restrictions. Now, let's talk about building  applications using AI Models. There are several use cases for GenAI-powered  applications across various domains,  
including Marketing, Customer Support,  Business and Finance, and Education. Let's say we want to build a chatbot that uses an  LLM to provide personalized book recommendations. First, we'd choose an LLM Provider,  
considering factors like pricing,  availability, and API documentation. getting an API key and  installing necessary libraries.
planning out questions to gather user preferences  and determine how to present recommendations. For implementation, we'd use a web  framework to build the application,  
creating a user interface and the  backend logic to handle interactions. We'd then integrate the LLM, defining  prompts to generate personalized book   After processing and  displaying the recommendations,  
Finally, we'd deploy the application   and set up monitoring to track  performance and user interactions. This process showcases how we can  leverage GenAI to create intelligent,  
personalized applications that  provide value in specific domains. Now, let's talk about making AI models your own.  Imagine having a model that's not just smart,   but smart about your specific needs.  That's what we're aiming for here.
Retrieval-Augmented Generation  (RAG) and Fine-Tuning. Think of RAG as giving your AI  model a personalized library.
your databases, documents, even  the internet - in real-time. This means the model can pull in  the most up-to-date and relevant   Here's how RAG works: When a user asks a question,
the system first searches its external  sources for relevant information. It then feeds this information to  the AI model along with the question. The model then crafts an answer using both this  retrieved information and its own knowledge.
a lot but also knows exactly where  to look for additional information. of information retrieval and language generation.
when dealing with complex questions that require  synthesizing information from multiple sources. This technique adapts a pre-trained  AI model to your specific needs,  
improving its performance  on domain-specific tasks. which has broad knowledge from training  on vast amounts of general data. Fine-tuning then tailors this model to  your specific domain or task dataset.
We test the model's performance using a  validation set and refine the process as needed. The result is a model that combines broad  knowledge with expertise in your domain. By fine-tuning, you create an AI that  understands your unique challenges and  
Generative AI opens up a world of  possibilities for developers and businesses. There are numerous GenAI models  and platforms to choose from to  
build innovative applications  and solve complex problems. If you like our video, you might like  our system design newsletter as well.   Trusted by 1,000,000 readers.
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