uCertify

Building AI Agents with LLMs, RAG, and Knowledge Graphs

(AI-AGENT.AJ1)
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Skills You’ll Get

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1

Preface

  • Who this course is for
  • What this course covers
  • To get the most out of this course
  • Download the example code files
  • Conventions used
2

Analyzing Text Data with Deep Learning

  • Technical requirements
  • Representing text for AI
  • Embedding, application, and representation
  • RNNs, LSTMs, GRUs, and CNNs for text
  • Performing sentiment analysis with embedding and deep learning
  • Summary
3

The Transformer: The Model Behind the Modern AI Revolution

  • Technical requirements
  • Exploring attention and self-attention
  • Introducing the transformer model
  • Training a transformer
  • Exploring masked language modeling
  • Visualizing internal mechanisms
  • Applying a transformer
  • Summary
4

Exploring LLMs as a Powerful AI Engine

  • Technical requirements
  • Discovering the evolution of LLMs
  • Instruction tuning, fine-tuning, and alignment
  • Exploring smaller and more efficient LLMs
  • Exploring multimodal models
  • Understanding hallucinations and ethical and legal issues
  • Prompt engineering
  • Summary
5

Building a Web Scraping Agent with an LLM

  • Technical requirements
  • Understanding the brain, perception, and action paradigm
  • Classifying AI agents
  • Understanding the abilities of single-agent and multiple-agent systems
  • Exploring the principal libraries
  • Creating an agent to search the web
  • Summary
6

Extending Your Agent with RAG to Prevent Hallucinations

  • Technical requirements
  • Exploring naïve RAG
  • Retrieval, optimization, and augmentation
  • Evaluating the output
  • Comparison between RAG and fine-tuning
  • Using RAG to build a movie recommendation agent
  • Summary
7

Advanced RAG Techniques for Information Retrieval and Augmentation

  • Technical requirements
  • Discussing naïve RAG issues
  • Exploring the advanced RAG pipeline
  • Modular RAG and its integration with other systems
  • Implementing an advanced RAG pipeline
  • Understanding the scalability and performance of RAG
  • Open questions and future perspectives
  • Summary
8

Creating and Connecting a Knowledge Graph to an AI Agent

  • Technical requirements
  • Introduction to knowledge graphs
  • Creating a knowledge graph with your LLM
  • Retrieving information with a knowledge graph and an LLM
  • Understanding graph reasoning
  • Ongoing challenges in knowledge graphs and GraphRAG
  • Summary
9

Reinforcement Learning and AI Agents

  • Technical requirements
  • Introduction to reinforcement learning
  • Deep reinforcement learning
  • LLM interactions with RL models
  • Key takeaways
  • Summary
10

Creating Single- and Multi-Agent Systems

  • Technical requirements
  • Introduction to autonomous agents
  • Working with HuggingGPT
  • Multi-agent system
  • SaaS, MaaS, DaaS, and RaaS
  • Summary
11

Building an AI Agent Application

  • Technical requirements
  • Introduction to Streamlit
  • Developing our frontend with Streamlit
  • Creating an application with Streamlit and AI agents
  • Machine learning operations and LLM operations
  • Asynchronous programming
  • Docker
  • Summary
12

The Future Ahead

  • AI agents in healthcare
  • AI agents in other sectors
  • Challenges and open questions
  • Summary

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