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[73% Off] Generative Ai With Python

LLMs, Vector DBs, RAG, Agentic Systems, and more

Added on August 28, 2025 Development 2 min read

What you’ll learn

• Go beyond basic chatbots and learn to harness the intelligence of Large Language Models (LLMs) using Python.
• Discover how to create and leverage Vector Databases to store and efficiently retrieve information for your AI applications.
• Learn the cutting-edge technique that allows your AI to answer complex questions using your own data sources
• making it smarter and more accurate.
• Explore the fascinating world of Agentic Systems and build autonomous AI agents that can perform tasks
• make decisions
• and interact with their environment.
• Get hands-on experience building practical projects that showcase the power and versatility of generative AI.
• Understand the fundamental concepts behind generative AI and gain the practical Python skills to bring your ideas to life.
• Acquire a deep understanding of the core technologies driving the next generation of intelligent applications.

Requirements

• Basic Python knowledge is required – you should know about basic data types
• how to implement loops
• or how to write functions.

Description

You’ll delve into the fundamentals of Large Language Models (LLMs) and the crucial role of Vector Databases for efficient information retrieval. Discover the power of Retrieval-Augmented Generation (RAG), which allows your AI to answer complex questions using your own data, making it smarter and more contextually aware.

Furthermore, you’ll explore the exciting domain of Agentic Systems, learning how to design and build autonomous AI agents capable of performing tasks and making decisions.

In my course I will teach you:

• Large-Language Models

  • Classical NLP vs. LLM

  • Narrow AI Achievements

  • Model Performance and Achievements

  • Model Training Process

  • Model Improvement Options

  • Model Providers

  • Model Benchmarking

  • Interaction with LLMs

  • Message Types

  • LLM Parameters

  • Local Use of Models

  • Large Multimodal Models

  • Tokenization

  • Reasoning Models

  • Small Language Models

  • JailBreaking

  • Working with Chains

  • Parallel Chains, Router Chains, …

• Vector Databases

  • Data Ingestion Pipeline

  • Data source and data loading

  • data chunking

  • embeddings

  • data storage

  • data querying

• Retrieval-Augmented Generation

  • Baseline RAG

  • Context Enrichment

  • Corrective RAG

  • Hybrid RAG

  • Query Expansion

  • Speculative RAG

  • Agentic RAG

• Agentic Systems

  • crewAI

  • Google ADK

  • OpenAI Agents SDK

  • AG2

  • LangGraph (coming soon)

• Agent Interactions

  • MCP

  • ACP

  • A2A