Generative AI with Python

Generative AI with Python

LLMs, LangChain, and Advanced Techniques

Build intelligent, production-ready AI systems with Python, leveraging cutting-edge LLMs and frameworks in 4 days

AI is everywhere these days, and if you're a Python developer, you've probably wondered how to actually build something useful with all these powerful language models everyone's talking about. Sure, ChatGPT is impressive, but how do you integrate that kind of intelligence into your own applications?

This course cuts through the hype and shows you exactly how to do it. We'll dive into the practical stuff—connecting to APIs from OpenAI, Hugging Face, and DeepSeek, working with frameworks like LangChain, and building systems that can actually retrieve and use information intelligently (that's the RAG part, if you're curious). You'll also learn how to write prompts that actually get the AI to do what you want, which is trickier than it sounds.

Here's the thing: this isn't another theory-heavy course that leaves you wondering "okay, but how do I actually use this?" Our instructor has been building real software for over 30 years, so everything you'll learn comes from solving actual problems that companies face today.

By the end, you'll know how to build AI-powered applications that people will actually want to use—not just cool demos that fall apart in the real world.

Learning Outcomes

By the end of this course, participants will be able to:

• Integrate and utilize APIs from OpenAI, Hugging Face, and DeepSeek within Python applications.
• Implement advanced ML and DL techniques pertinent to generative AI.
• Develop applications using LangChain and RAG frameworks for efficient data retrieval and processing.
• Apply prompt engineering strategies to optimize interactions with LLMs.
• Employ Python's advanced features, including decorators, recursion, and parallel computing, to enhance application performance.

Prerequisites

Participants should have:

• Professional Python programming experience.
• Proficiency in object-oriented programming and asynchronous programming.
• A solid understanding of machine learning and deep learning fundamentals.
• Experience with RESTful APIs and JSON data structures.
• Familiarity with Python libraries such as NumPy, pandas, and scikit-learn.

Training Course Outline

1. Introduction to Generative AI and LLMs

• Overview of Generative AI and its applications.
• Understanding Large Language Models (LLMs): Architecture and capabilities.
• Ethical considerations and challenges in deploying LLMs.

2. Advanced Machine Learning and Deep Learning Concepts

• Review of neural networks and deep learning architectures.
• Transfer learning and fine-tuning pre-trained models.
• Implementing custom models for specific generative tasks.

3. API Integration with Leading LLM Providers

• Setting up and authenticating with OpenAI, Hugging Face, and DeepSeek APIs.
• Handling API responses and error management.
• Best practices for efficient and secure API usage.

4. LangChain Framework for LLM Applications

• Introduction to LangChain and its components.
• Building chains and agents for complex tasks.
• Integrating memory and state management in applications.

5. Retrieval-Augmented Generation (RAG) Techniques

• Understanding the RAG architecture and its benefits.
• Implementing RAG for document retrieval and question answering.
• Optimizing RAG pipelines for performance and accuracy.

6. Prompt Engineering for Efficient Programming

• Designing effective prompts for various tasks.
• Techniques for refining and iterating prompts.
• Automating prompt generation and evaluation.

7. Advanced Python Techniques

• Decorators:
  • Creating and applying decorators for code modularity.
  • Use cases in logging, authentication, and performance monitoring.
• Recursion:
  • Implementing recursive solutions for complex data structures.
  • Optimizing recursive functions for performance.
• Parallel Computing:
  • Utilizing multiprocessing and multithreading in Python.
  • Best practices for concurrent execution and resource management.

8. Building Real-World Applications

• Developing a chatbot using integrated LLM APIs and LangChain.
• Creating a document summarization tool with RAG.
• Implementing a code generation assistant using prompt engineering.

9. Deployment and Scaling

• Containerizing applications with Docker.
• Deploying applications on cloud platforms (e.g., AWS, Azure).
• Monitoring and maintaining application performance.

10. Capstone Project

• Participants will conceptualize, design, and develop a comprehensive application incorporating LLM integration, LangChain, and advanced Python techniques.
• Projects will be presented and reviewed, focusing on real-world applicability and innovation.

This course is designed to equip participants with the skills and knowledge required to build sophisticated, efficient, and scalable AI applications using Python and the latest advancements in generative AI.