AI Camp: Mastering GenAI – Building Intelligent Agents & Chatbot for Businesses

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AI Camp: Mastering GenAI – Building Intelligent Agents & Chatbot for Businesses

Categories AI, Chatbot, Programming, Python

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Course Prerequisite(s)

Please note that this course has the following prerequisites which must be completed before it can be accessed
Introduction to Python for Data Science

Master prompt engineering: Create effective prompts to optimize LLM responses.
Build RAG workflows: Combine document retrieval and LLMs for accurate, contextual Q&A systems.
Implement multi-agent systems: Develop intelligent, cooperative agent architectures.
Fine-tune LLMs: Use techniques like PEFT, LoRA, and QLoRA to customize models for domain-specific needs.
Optimize for production: Learn strategies for deployment, scalability, and GPU inference optimization.
Create industry-specific solutions: Apply your skills to build chatbots tailored to real-world applications like healthcare, e-commerce, or education.

Course Curriculum

Introduction to LLMs, Prompt Engineering, and RAG Basics
Begin the quarter with an introduction to the Q&A chatbot project and the fundamentals of LLMs. Learn prompt engineering techniques and create your first GPT-based application. Explore embeddings and build a basic RAG application from scratch in Python.

End-to-End RAG Applications
Learn to deploy a complete RAG application and make it production-grade using LangChain. Build workflows for document-based Q&A.

Multi-Agent Systems with LangGraph
Explore multi-agent architectures and learn how to develop agent-based applications using LangGraph. Build cooperative agents that solve complex tasks.

Synthetic Data Generation and RAG Evaluation
Generate synthetic datasets for training and evaluation. Learn how to use LangSmith to assess the performance of your RAG application.

Fine-Tuning LLMs and Embedding Models
Learn fine-tuning techniques like Parameter Efficient Fine-Tuning (PEFT), LoRA, and QLoRA for embedding models. Use open-source tools to enhance your chatbot’s domain-specific capabilities.

Mini Project and Industry Use Cases
Explore industry-specific use cases for LLM-based applications. Prototype a solution, generate synthetic test datasets, and refine your chatbot with new functionality.

Advanced Prompting and RAG Optimization
Optimize RAG applications with advanced techniques like Multi-Query Retrieval, Semantic Chunking, and Reciprocal Rank Fusion (RRF) using DSPy.

Production-Ready Strategies and Endpoints
Learn strategies for deploying a scalable and performant RAG application. Set up open-source endpoints and prepare your chatbot for production use.

On-Prem RAG and Inference Optimization
Learn how to deploy RAG applications on-premises using LangServe. Optimize inference and serving with GPUs for production-grade performance.

Capstone: Build and Present an End-to-End AI Chatbot
Finalize your Q&A chatbot project by implementing advanced features and ensuring it is production-ready. Deploy the chatbot to a scalable endpoint, test its performance, and showcase its business value.

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Level

Intermediate
Duration

72 hours
Last Updated

December 12, 2024
Certificate

Certificate of completion

A course by

DataU Instructor

Instructor

Aspiring AI developers looking to specialize in LLMs and chatbot development.
Data scientists and engineers eager to learn advanced RAG techniques and fine-tuning LLMs.
Professionals in any industry who want to build domain-specific intelligent applications.
Students and enthusiasts with a passion for AI and solving real-world problems with intelligent systems.
Anyone aiming to master cutting-edge tools like LangChain, LangGraph, and open-source LLM frameworks.

AI Camp: Mastering GenAI – Building Intelligent Agents & Chatbot for Businesses

Course Prerequisite(s)

What I will learn?

Course Curriculum

End-to-End RAG Applications
Learn to deploy a complete RAG application and make it production-grade using LangChain. Build workflows for document-based Q&A.

Multi-Agent Systems with LangGraph
Explore multi-agent architectures and learn how to develop agent-based applications using LangGraph. Build cooperative agents that solve complex tasks.

Synthetic Data Generation and RAG Evaluation
Generate synthetic datasets for training and evaluation. Learn how to use LangSmith to assess the performance of your RAG application.

Fine-Tuning LLMs and Embedding Models
Learn fine-tuning techniques like Parameter Efficient Fine-Tuning (PEFT), LoRA, and QLoRA for embedding models. Use open-source tools to enhance your chatbot’s domain-specific capabilities.

Mini Project and Industry Use Cases
Explore industry-specific use cases for LLM-based applications. Prototype a solution, generate synthetic test datasets, and refine your chatbot with new functionality.

Advanced Prompting and RAG Optimization
Optimize RAG applications with advanced techniques like Multi-Query Retrieval, Semantic Chunking, and Reciprocal Rank Fusion (RRF) using DSPy.

Production-Ready Strategies and Endpoints
Learn strategies for deploying a scalable and performant RAG application. Set up open-source endpoints and prepare your chatbot for production use.

On-Prem RAG and Inference Optimization
Learn how to deploy RAG applications on-premises using LangServe. Optimize inference and serving with GPUs for production-grade performance.

Capstone: Build and Present an End-to-End AI Chatbot
Finalize your Q&A chatbot project by implementing advanced features and ensuring it is production-ready. Deploy the chatbot to a scalable endpoint, test its performance, and showcase its business value.

Earn Your Certificates

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AI Camp: Mastering GenAI – Building Intelligent Agents & Chatbot for Businesses

Course Prerequisite(s)

What I will learn?

Course Curriculum

End-to-End RAG Applications Learn to deploy a complete RAG application and make it production-grade using LangChain. Build workflows for document-based Q&A.

Multi-Agent Systems with LangGraph Explore multi-agent architectures and learn how to develop agent-based applications using LangGraph. Build cooperative agents that solve complex tasks.

Synthetic Data Generation and RAG Evaluation Generate synthetic datasets for training and evaluation. Learn how to use LangSmith to assess the performance of your RAG application.

Fine-Tuning LLMs and Embedding Models Learn fine-tuning techniques like Parameter Efficient Fine-Tuning (PEFT), LoRA, and QLoRA for embedding models. Use open-source tools to enhance your chatbot’s domain-specific capabilities.

Mini Project and Industry Use Cases Explore industry-specific use cases for LLM-based applications. Prototype a solution, generate synthetic test datasets, and refine your chatbot with new functionality.

Advanced Prompting and RAG Optimization Optimize RAG applications with advanced techniques like Multi-Query Retrieval, Semantic Chunking, and Reciprocal Rank Fusion (RRF) using DSPy.

Production-Ready Strategies and Endpoints Learn strategies for deploying a scalable and performant RAG application. Set up open-source endpoints and prepare your chatbot for production use.

On-Prem RAG and Inference Optimization Learn how to deploy RAG applications on-premises using LangServe. Optimize inference and serving with GPUs for production-grade performance.

Capstone: Build and Present an End-to-End AI Chatbot Finalize your Q&A chatbot project by implementing advanced features and ensuring it is production-ready. Deploy the chatbot to a scalable endpoint, test its performance, and showcase its business value.

Earn Your Certificates

Student Ratings & Reviews

A course by

Requirements

Tags

Target Audience

PROGRAMS

COMPANY

COMMUNITY

End-to-End RAG Applications
Learn to deploy a complete RAG application and make it production-grade using LangChain. Build workflows for document-based Q&A.

Multi-Agent Systems with LangGraph
Explore multi-agent architectures and learn how to develop agent-based applications using LangGraph. Build cooperative agents that solve complex tasks.

Synthetic Data Generation and RAG Evaluation
Generate synthetic datasets for training and evaluation. Learn how to use LangSmith to assess the performance of your RAG application.

Fine-Tuning LLMs and Embedding Models
Learn fine-tuning techniques like Parameter Efficient Fine-Tuning (PEFT), LoRA, and QLoRA for embedding models. Use open-source tools to enhance your chatbot’s domain-specific capabilities.

Mini Project and Industry Use Cases
Explore industry-specific use cases for LLM-based applications. Prototype a solution, generate synthetic test datasets, and refine your chatbot with new functionality.

Advanced Prompting and RAG Optimization
Optimize RAG applications with advanced techniques like Multi-Query Retrieval, Semantic Chunking, and Reciprocal Rank Fusion (RRF) using DSPy.

Production-Ready Strategies and Endpoints
Learn strategies for deploying a scalable and performant RAG application. Set up open-source endpoints and prepare your chatbot for production use.

On-Prem RAG and Inference Optimization
Learn how to deploy RAG applications on-premises using LangServe. Optimize inference and serving with GPUs for production-grade performance.

Capstone: Build and Present an End-to-End AI Chatbot
Finalize your Q&A chatbot project by implementing advanced features and ensuring it is production-ready. Deploy the chatbot to a scalable endpoint, test its performance, and showcase its business value.