Generative AI
Engineer

 Foundations of AI and Introduction to Prompt Engineering Introduction to AI

• Overview of Generative AI

• Basic principles of AI and machine learning

• Detailed explanation of Generative Pretrained Transformers (GPT)

• The role of prompt engineering in AI applications

Environment and Tools Setup (Hand on IDE Setup)

• The role of prompt engineering in AI applications

• VENV

• GitHub

Python Review and Initial Scripting (Lab with Colab and Jupyter Notebook in VScode)

• Review Basic Python Command (VS Code)

• Refresher on Python essential for AI : Pandas NumPy (VS Code)

• Review ML Process : Linear Regression/Logistic Regression (VScode)

• NLP Task : Basic Text Classification Lab. (VS Code-Colab)

•  Working with LLM and OpenAI's API Large Language Models (LLMs) (Demo with Colab)

  • Understanding LLM

  • Building LLM models with nano GPT

  • Overview of Hugging Face and open source LLMs

• Local LLM (Lab with Jupyter Notebook in VScode)

  • Introduction to Ollama

  • Setting up and experimenting with local models

  • Practical interface with local LLMs

  • Text Classification with LLM

• Getting Started with OpenAI's API (Lab with Jupyter Notebook inVScode)

  • How to set up an OpenAI account and generate API keys

  • Guidelines for Prompting

  • Iterative Prompt Development

  • Summarizing, Inferring and Transforming Chatbot

• Building Applications and Integrating AI Deep Dive into OpenAI’s Assistant API
  (Lab with Jupyter Notebook in VScode)

  • Overview of Assistant API

  • Lab Conversation with Assistant API

  • Introduction to Retrieval Augment Generation (RAG)

  • Lab RAG with Assistant API

• A Lang Chain Framework (Lab with Jupyter Notebook in VScode)

  • Introduction to Lang Chain Sending 

  • Models, Prompts and Output

  • memory, Chain in Lang Chain

• Building Applications and Integrating AI Deep Dive into OpenAI’s Assistant API
  (Lab with Jupyter Notebook in VScode)

  • Overview of Assistant API

  • Lab Conversation with Assistant API

  • Introduction to Retrieval Augment Generation (RAG)

  • Lab RAG with Assistant API

• A Lang Chain Framework (Lab with Jupyter Notebook in VScode)

  • Introduction to Lang Chain Sending 

  • Models, Prompts and Output

  • memory, Chain in Lang Chain

• Advanced Techniques and Practical Applications AI-Enhanced Web Projects (Hand on Project)

  • Overview of Streamlit

  • Building a simple web application to utilize AI features.

  • Project: Abdul Chat, integrating AI into a chatbot

• Introduction to Retrieval-Augmented Generation (RAG) (Hand on Project)

  • Concepts and practical use of RAG

  • Project: AbdulPDF, a chatbot with knowledge from PDF files

• Fine-Tuning Large Language Model (Demo Project)

  • Understanding model fine-tuning techniques

  • Hands-on session on

  • adjusting models for specific use cases