Introduction to AI Product Development

Picking the stack - Navigating JavaScript and Python

Designing a Hybrid Web Application Architecture with JavaScript and Python

Streaming events with Server-Sent Events and WebSockets

System Design for AI applications

Discovering the OpenAI Completion API

Handling Server-Sent Events with JavaScript

Building the useCompletion hook

Rendering Completion Output

Mocking Streams

Testing the useCompletion hook

In this module, we're diving into the creation of an AI product with a frontend focus.

Building the Front-End

Creating a FastAPI server

Exploring asynchronous programming in Python

Integrating Langchain with FastAPI for Asynchronous Streaming

Testing with PyTest and LangChain

In this module, we're diving into the creation of an AI product with a Backend focus.

Building the Backend

Building the useChat hook

Building the User Interface

In this module, we learn how to build a chat.

Building a Chat

Discovering Retrieval Augmented Generation

Building a Semantic Search Engine with Chroma

Adding Retrieval-Augmented Generation to the chat

In this module, we implement Retrieval Augmented Generation"

Implementing Retrieval Augmented Generation

Final words

Conclusion

Exploring AI Agents - Toward Autonomous Systems

Building an Asynchronous Event-Driven Service Using Context Variables

Adding Tools To The Chat

Building a Generative UI

In this lessons we will explore how AI agents function with a focus on autonomy, tool usage, and self-correction. They will also be introduced to the potential future developments in AI agent technology.

Building an Agent

Join us as we dive into the world of AI-powered streaming text and chat UIs using TypeScript, React, and Python. 

Large Language Models (LLMs) are revolutionizing various industries, but creating simple demos in notebooks or with tools like Gradio is not enough. To truly harness the potential of these models, we need to build sophisticated, real-world applications.

This comprehensive course is structured into six detailed modules where you will:
- Develop a dynamic front-end with React.
- Integrate FastAPI with LangChain to create a robust backend.
- Utilize Server-Sent Events (SSE) for seamless real-time data streaming.

This comprehensive course comes packed with valuable extras:
- The complete source code under MIT licence
- Exclusive access to our community forum
- Detailed exploration of Retrieval-Augmented Generation (RAG) for enhancing model responses
- Hands-on experience with developing intelligent agents capable of maintaining context in conversations

Taught by [Louis Sanna](https://www.linkedin.com/in/louis-sanna-0168363a/), a seasoned Tech Lead with expertise in GenAI applications and digital transformation projects for major organizations like UNESCO and Renault. His deep industry insights and practical experience will guide you from basics to advanced implementations.

Taking this course will spare you countless days of troubleshooting and challenges, offering a shortcut through the learning curve. Moreover, it presents a unique opportunity to engage with one of the most significant technological trends of our era. This is your chance to become a part of the groundbreaking wave of LLM advancements, positioning you at the forefront of innovation.

### Problem this Course Solves

This course is designed to tackle specific problems that AI engineers face when integrating LLM capabilities into web applications. Streaming text presents a unique challenge for engineers, requiring the integration of multiple technologies and concepts. This course addresses this by:

- Exploring the use of Langchain for seamless LLM provider switching. Given Langchain's rapid development, understanding its streaming, asynchronous, and callback functionalities can be daunting.
- Demystifying asynchronous programming in Python, which can be more complex than in JavaScript. We'll cover the differences between asynchronous and synchronous programming, the benefits of asynchronous operations, and how to implement and integrate asynchronous function calls within a streaming context.
- Selecting and implementing a protocol for streaming text from the backend to the frontend. We will explain why Server-Sent Events (SSE) is an ideal standard, despite being relatively new and unfamiliar to many.
- Creating an SSE endpoint using a Python server framework, such as FastAPI, to facilitate real-time data streaming.
- Reading the stream in the front-end using the ReadableStream interface of the Fetch API, ensuring efficient data handling.
- Updating the React state of prompts with consideration for React's batching behavior to prevent unexpected issues during rapid updates.
- Integrating custom components within text responses to enable dynamic, on-the-fly generative UIs.

By the end of this course, you will have a comprehensive understanding of how to effectively integrate and manage LLM capabilities within web applications, overcoming the technical hurdles associated with real-time text streaming.

### Preview the Application

Experience the power of what you'll build firsthand. Try out the demo app we will develop during the course:
[The Demo App](https://build-ai-driven-web-applications.vercel.app/)

Enroll now and start building transformative AI-powered applications that streamline communication and enhance user engagement!

### Course Content Overview

##  Basic Package
- Total Modules: **6**
- Total Lessons: **20**
- Total Video Runtime: **1 hour, 10 minutes**

##  Advanced Package
- Total Modules: **7**
- Total Lessons: **24**
- Total Lines of Code: **3706**
- Total Video Runtime: **1 hour, 34 minutes**

Dive into Retrieval Augmented Generation and Autonomous Agents with LangChain, Chroma and FastAPI

Large Language Models are reshaping industries, yet integrating them into real-time streaming UIs presents unique challenges. In this course we will learn how to seamlessly integrate LLM APIs into applications and build AI-powered streaming text and chat UIs with TypeScript, React, and Python. Step-by-step, we will build a full-stack AI application with quality code and very flexible implementation.

The LLM application in this course includes:
- Completion Use-Case (english to emojis)
- Chat
- Retrieval Augmented Generation use-case
- AI Agent Use-Cases (code execution, data-Analyste agent)

This app can be used as a starting point in most projects, saving a huge amount of time, and its flexibilty allows new tools to be added as needed.

At the end of this course, you will have mastered end-to-end implementation of a flexible and high-quality LLM application. This course will also equip you with the knowledge and skills necessary to create sophisticated LLM solutions of your own.

Responsive LLM Applications with Server-Sent Events

How to design systems for AI applications

How to stream the answer of a Large Language Model

Differences between Server-Sent Events and WebSockets

How asynchronous programming in Python works

What problems Retrieval Augmented Generation can solve

In this lesson, we're going to decide on our technology stack. The choice of tools is crucial to the success of any project, and often, we're not the first ones tackling a specific issue, allowing us to benefit from existing libraries and code samples available on platforms like Stack Overflow and GitHub.

When considering the development of an AI product, two primary technologies come to mind: JavaScript and Python.

Let's look into why JavaScript first. Looking at the usage statistics of client-side languages, JavaScript's dominance is evident, boasting a 

. This prevalence is largely because JavaScript is the language of the web browser, enabling direct execution within the browser's console.

However, Python is another contender, especially in the realms of data science and machine learning, where it is considered paramount. Observing surveys from machine learning competitions, we find that Python was used in more than 

. This is due to the extensive range of powerful machine learning frameworks and libraries available in Python.

Now, the question arises: do we have to choose one over the other, or can we find a way to use both? Initially, we'll explore using either JavaScript or Python exclusively to create an AI product, but we'll also consider how merging both could offer the best of both worlds.

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title: Picking the stack - Navigating JavaScript and Python
privateVideoUrl: https://stream.mux.com/k01Gdwgb7PMwgBkVo6hY020002zLHmcZPUexuo98mlrzKt8.m3u8
---

# Deciding on Our Technology Stack

In this lesson, we're going to decide on our technology stack. The choice of tools is crucial to the success of any project, and often, we're not the first ones tackling a specific issue, allowing us to benefit from existing libraries and code samples available on platforms like Stack Overflow and GitHub.

### JavaScript vs Python

When considering the development of an AI product, two primary technologies come to mind: JavaScript and Python.

Let's look into why JavaScript first. Looking at the usage statistics of client-side languages, JavaScript's dominance is evident, boasting a [staggering 98.9% usage](https://app.excalidraw.com/s/A8BjLXjdYlk/4PaYVWHqbil). This prevalence is largely because JavaScript is the language of the web browser, enabling direct execution within the browser's console.

However, Python is another contender, especially in the realms of data science and machine learning, where it is considered paramount. Observing surveys from machine learning competitions, we find that Python was used in more than [95% of the winning solutions](https://mlcontests.com/state-of-competitive-machine-learning-2023/#winning-toolkit). This is due to the extensive range of powerful machine learning frameworks and libraries available in Python.

Now, the question arises: do we have to choose one over the other, or can we find a way to use both? Initially, we'll explore using either JavaScript or Python exclusively to create an AI product, but we'll also consider how merging both could offer the best of both worlds.