Building Your First AI Assistant with Large Language Models

Building Your First AI Assistant with Large Language Models, available at $44.99, has an average rating of 3.67, with 77 lectures, based on 3 reviews, and has 42 subscribers.

You will learn about Comprehensive understanding of how to build, deploy, and maintain an AI assistant through a hands-on case study based on Scopio (portfolio AI assistant) How to create AI assistants using both the OpenAI Assistants API and LangChain. We will compare the advantages and disadvantages of each approach. Fundamental tools in LangChain for developing applications based on Large Language Models, including the usage of LangServe and LangSmith. Integration of Retrieval Augmented Generation (RAG) pipelines with your AI-based applications. This course is ideal for individuals who are Aspiring AI professionals seeking to understand the fundamentals of AI assistants and LLMs. Developers and software engineers interested in expanding their skills into the realm of AI and natural language processing. Students and educators looking for a comprehensive resource on AI and machine learning principles. Anyone with a curiosity about how AI can be used to enhance user experiences on digital platforms. It is particularly useful for Aspiring AI professionals seeking to understand the fundamentals of AI assistants and LLMs. Developers and software engineers interested in expanding their skills into the realm of AI and natural language processing. Students and educators looking for a comprehensive resource on AI and machine learning principles. Anyone with a curiosity about how AI can be used to enhance user experiences on digital platforms.

Enroll now: Building Your First AI Assistant with Large Language Models

Summary

Title: Building Your First AI Assistant with Large Language Models

Price: $44.99

Average Rating: 3.67

Number of Lectures: 77

Number of Published Lectures: 77

Number of Curriculum Items: 78

Number of Published Curriculum Objects: 78

Original Price: $69.99

Quality Status: approved

Status: Live

What You Will Learn

Comprehensive understanding of how to build, deploy, and maintain an AI assistant through a hands-on case study based on Scopio (portfolio AI assistant)
How to create AI assistants using both the OpenAI Assistants API and LangChain. We will compare the advantages and disadvantages of each approach.
Fundamental tools in LangChain for developing applications based on Large Language Models, including the usage of LangServe and LangSmith.
Integration of Retrieval Augmented Generation (RAG) pipelines with your AI-based applications.

Who Should Attend

Aspiring AI professionals seeking to understand the fundamentals of AI assistants and LLMs. Developers and software engineers interested in expanding their skills into the realm of AI and natural language processing. Students and educators looking for a comprehensive resource on AI and machine learning principles. Anyone with a curiosity about how AI can be used to enhance user experiences on digital platforms.

Target Audiences

Aspiring AI professionals seeking to understand the fundamentals of AI assistants and LLMs. Developers and software engineers interested in expanding their skills into the realm of AI and natural language processing. Students and educators looking for a comprehensive resource on AI and machine learning principles. Anyone with a curiosity about how AI can be used to enhance user experiences on digital platforms.

Welcome to “Building Your First AI Assistant with Large Language Models.” It is a starting point to the fascinating world of Generative AI. This is a course for beginners, and it teaches you how to build an AI assistant (also called an “AI chatbot”) from the very ground up, similar to ChatGPT prompt engineering. It welcomes students of all ages and professionals looking to upgrade their skill set, or anyone who is interested in AI but needs a starting point.

Throughout the course, we will be working on an AI assistant called “Scopio,” that can answer questions and guide users through Scopic’s portfolio content.

This is not just a theoretical course. Every step, every decision, and every line of code we work on will go into building this assistant.

What You’ll Learn:

o Understanding AI: The basic concepts of AI and ML

o Building AI Assistants: Learn how to develop an AI assistant (“AI chatbot”) that understands and responds to user queries

o Create AI Assistants: This course teaches you how to make an AI assistant who can understand user queries and reply properly

o Natural Language Processing: Dive deep into tokenization, embeddings, and the transformer architecture

o Development Tools: Learn through experience with tools such as FastAPI for back-end development and OpenAI’s API for AI capabilities

o Real-World Exercise: Build, test, and deploy your AI Assistant into the real world

Course Features

· 4 extensive modules: Get ready for theoretical and practical lessons on AI, ML, and LLMs

· Hands-on project: Pass through a series of practical exercises to build an AI chatbot and refine as needed[EG1]

· Real-world application: Work on developing a real-life AI assistant named “Scopio”

· Expert guidance: Our instructor is ready to share insights and recommendations based on their comprehensive experience in AI

Why This Course

This course stands out for its practical application and industry relevance. By focusing on the creation of a real-world AI chatbot, you’ll gain skills that are highly sought after in today’s tech-driven industries.

The flexible, self-paced learning model and access to a supportive community ensure that you can learn at your own pace and seek help when needed.

Completing this course will empower you to contribute to technological advancements, innovate in your field, and open new career opportunities in AI and tech industries.

The course is:

– Self-paced

– Flexible

With comprehensive modules, hands-on projects, expert guidance, and a rich resource library, you’ll get the A to Z of building AI Assistants with LLMs.

Knowledge Requirements:

· Basic knowledge of programming is a plus (preferably in Python)

· Familiarity with the fundamentals of computer science will be beneficial (algebra, probability, etc.)

· No prior knowledge of AI or ML is required for the course

Course Toolkit Requirements:

· FastAPI for the backend of Python

· HTML, CSS, and JS basics

· An OpenAI API key

· A text editor or IDE for a coding environment

· Experience with using terminal or command prompt

This course is for:

· Aspiring AI professionals

· Developers and software engineers

· Students and educators

· Anyone curious about AI and its applications

What You’ll Learn:

· Understand the fundamentals of AI and machine learning

· Get an introduction to AI assistants and designing them

· Develop an AI assistant using natural language processing

· Implement tokenization, embeddings, and transformer architectures

· Use tools like FastAPI, OpenAI’s API, and web technologies for AI integration

· Gain hands-on experience with real-world AI applications

· Navigate and utilize AI development tools and platforms.

· Learn how to collect user feedback and improve the assistant.

· Enhance your skills and open new career opportunities in AI and tech.

Course Curriculum

Chapter 1: Introduction

Lecture 1: Introduction

Lecture 2: Scopio

Lecture 3: LLM Considerations

Lecture 4: Scopio Considerations

Chapter 2: Installation

Lecture 1: Important Update: OpenAI Recharge Recommendation

Lecture 2: Requirements Installation

Lecture 3: 2 OpenAI Key

Chapter 3: OpenAI Assistant

Lecture 1: OpenAI Assistant Introduction

Lecture 2: Fast API Configuration

Lecture 3: Scopic Portfolio Document

Lecture 4: OpenAI Assistant Creation

Lecture 5: Assistant Objects Explanation

Lecture 6: Setting Up OpenAI Assistant's API

Lecture 7: Thread Creation Python Code

Lecture 8: OpenAI Assistant Chat Endpoint Part 1

Lecture 9: OpenAI Assistant Chat Endpoint Part 2

Lecture 10: OpenAI Assistant Chat Endpoint Part 3

Lecture 11: Testing Assistant Chatbot Part 1

Lecture 12: Testing Assistant Chatbot Part 2

Lecture 13: Important Update on Assistants

Lecture 14: Testing Assistant Chatbot Part 3

Lecture 15: Introduction to Create The Assistant Through Code

Lecture 16: Minor Adjustment To Project Structure

Lecture 17: Adding The Create Assistant Function

Lecture 18: Explaining Create Assistant Documentation

Lecture 19: Completing The Create Assistant Function

Lecture 20: Replacing the Assistant Id From The Main Module

Lecture 21: Fixing Bugs And Testing Assistant

Chapter 4: Langchain Assistant

Lecture 1: 1 Langchain vs. OpenAI Assistants

Lecture 2: 2 Install Jupyter

Lecture 3: Loading Portfolio Document

Lecture 4: Document Splitting

Lecture 5: Create Vector Store

Lecture 6: Retrieve Documents From Vector Store

Lecture 7: Q&A With RAG Part 1

Lecture 8: Q&A With RAG Part 2

Lecture 9: Langsmith

Lecture 10: RAG With Memory Chain Part 1

Lecture 11: RAG With Memory Chain Part 2

Lecture 12: RAG With Memory Chain Part 3

Lecture 13: Testing RAG With Memory Chain

Lecture 14: Monitoring Chain With LangSmith

Lecture 15: Streaming

Lecture 16: Integrating Vector Store

Lecture 17: Integrating RAG With Memory Chain

Lecture 18: LangServe Introduction

Lecture 19: LangServe Documentation And Playground

Lecture 20: Feedback

Chapter 5: Deployment

Lecture 1: Deployment Diagram

Lecture 2: Docker Image Creation

Lecture 3: Changing Port and Checking Execution Env

Lecture 4: SAM Template Configuration

Lecture 5: IAM User Creation

Lecture 6: Create Access Key

Lecture 7: VM Installation

Lecture 8: Creating Ubuntu Virtual Machine

Lecture 9: Fixing VM Ubuntu Bug

Lecture 10: Install Guest Additions

Lecture 11: Code Installation

Lecture 12: Copy Project Within VM

Lecture 13: Sudo Error Correction

Lecture 14: AWS CLI Install

Lecture 15: AWS CLI Configuration

Lecture 16: SAM CLI Installation

Lecture 17: Docker Installation

Lecture 18: SAM Validate

Lecture 19: SAM Build Command

Lecture 20: SAM Deploy Error

Lecture 21: Adding Missing Policies

Lecture 22: Stack Deletion

Lecture 23: Cloud Formation Error Debugging

Lecture 24: SAM Deploy Successful

Lecture 25: Deployment Testing

Lecture 26: Debugging Lambda Function

Lecture 27: Langsmith Monitoring

Lecture 28: Create Simple Client

Lecture 29: Delete And Update The Application

Instructors

Jorge Felipe Gaviria Fierro
Machine Learning and AI Engineer
Scopic Inc
Software Development

Rating Distribution

1 stars: 1 votes
2 stars: 0 votes
3 stars: 0 votes
4 stars: 0 votes
5 stars: 2 votes

Frequently Asked Questions

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