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Course 3 : Generative AI with Large Language Models

About Course

Course Title: Advanced Topics in Natural Language Processing

Course Description: This advanced course delves into cutting-edge techniques and applications in natural language processing (NLP), focusing on the latest developments in Generative AI and Large Language Models (LLMs). Participants will gain hands-on experience and theoretical knowledge in text generation, prompting techniques, embeddings with OpenAI GPT, question answering, retrieval augmented generation (RAG), preprocessing unstructured data, and leveraging knowledge graphs for RAG applications.

Course Outline:

  1. Exploring Generative AI & LLMs
    • Introduction to Generative AI and its applications in NLP
    • Overview of Large Language Models (LLMs) and their architecture
    • Case studies and examples of state-of-the-art LLMs
  2. Text Generation
    • Techniques for text generation using LLMs
    • Fine-tuning LLMs for specific text generation tasks
    • Generating coherent and diverse text outputs
  3. Techniques in Prompting and Prompt Engineering
    • Understanding the importance of prompts in LLMs
    • Prompting strategies for controlling and directing LLM outputs
    • Advanced prompt engineering techniques
  4. Embeddings with OpenAI GPT
    • Introduction to word embeddings and their role in NLP
    • Utilizing OpenAI GPT for generating embeddings
    • Fine-tuning embeddings for downstream NLP tasks
  5. Question Answering – using embeddings
    • Overview of question answering systems
    • Leveraging embeddings for question answering tasks
    • Case studies and practical applications
  6. Retrieval Augmented Generation (RAG) – Building Applications with Vector Databases
    • Understanding retrieval augmented generation (RAG)
    • Building RAG systems using vector databases
    • Case studies and hands-on projects
  7. Preprocessing Unstructured Data for LLM Applications
    • Techniques for preprocessing unstructured data
    • Data cleaning, normalization, and tokenization
    • Handling different data formats and sources
  8. Knowledge Graphs for RAG
    • Introduction to knowledge graphs and their relevance in RAG
    • Building knowledge graphs for storing and retrieving information
    • Integrating knowledge graphs with RAG systems for enhanced performance
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What Will You Learn?

  • Generative AI Fundamentals
  • Advanced Text Generation Techniques
  • Prompt Engineering Strategies
  • Embeddings and OpenAI GPT
  • Effective Question Answering
  • Retrieval Augmented Generation (RAG)
  • Vector Databases in Applications
  • Preprocessing Unstructured Data
  • Leveraging Knowledge Graphs
  • Practical Application Development

Course Content

Exploring Generative AI & LLMs

  • List of popular LLMs (OpenAI, Llama, BLOOM…) – key features and comparison
  • connecting to OpenAI
  • connecting to Azure OpenAI

Text generation

Techniques in Prompting and Prompt Engineering

Embeddings with OpenAI GPT

Question Answering – using embeddings

Retrieval Augmented Generation (RAG) – Building Applications with Vector Databases
Master the development of six innovative applications leveraging vector databases: semantic search, retrieval-augmented generation (RAG), anomaly detection, hybrid search, image similarity search, and recommender systems. Each application harnesses a distinct dataset to power its functionalities. Explore the creation of six dynamic applications employing vector databases and deploy them using Pinecone. Craft a hybrid search application amalgamating text and images to enhance multimodal search outcomes. Discover the process of constructing an application that quantifies and prioritizes facial similarity.

Preprocessing Unstructured Data for LLM Applications

Knowledge Graphs for RAG
Knowledge graphs serve as indispensable tools in software development, enabling the structuring of intricate data relationships, empowering intelligent search functionalities, and facilitating the creation of robust AI applications capable of reasoning across diverse data types. These graphs possess the unique ability to interlink data originating from both structured and unstructured sources, such as databases and documents, presenting an intuitive and adaptable approach to modeling complex real-world scenarios. Unlike conventional data representations like tables or simplistic lists, knowledge graphs excel in capturing the underlying meaning and contextual nuances of the data they encapsulate. This capability enables users to uncover profound insights and connections that might elude detection when utilizing traditional database structures. The rich, structured context provided by knowledge graphs proves invaluable, particularly in enhancing the output of large language models (LLMs). By leveraging knowledge graphs, developers can furnish LLMs with more pertinent context, surpassing the capabilities offered by semantic search in isolation. In essence, knowledge graphs serve as dynamic frameworks that not only facilitate efficient data organization but also empower AI systems with the contextual understanding necessary to generate more relevant and insightful outputs.

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