Overview:

This course takes you from Generative AI fundamentals to building sophisticated multi-agent AI ecosystems capable of reasoning, collaborating, and acting autonomously. You’ll learn how to design intelligent workflows that combine large language models, retrieval systems, and agent orchestration to solve real-world problems. By the end, you’ll be ready to deploy end-to-end AI-powered applications with confidence.

Module 1: Generative AI Foundations & Prompt Engineering

• Understanding the AI Landscape: From traditional AI to deep learning to generative models.

• Generative AI Platforms & Use Cases: Text, image, audio, and multi-modal systems.

• Prompt Design Principles: Role-based prompts, chain-of-thought, and scenario-driven instructions.

• Ethics & Safety: Preventing bias, misinformation, and unsafe outputs.

Module 2: Working with AI APIs & Applied Prompting

• Using Model APIs Effectively: Connecting to LLMs, managing authentication.

• Embedding & Semantic Search Basics.

• Debugging AI Responses: Managing unpredictable or inconsistent outputs.

• Structured Outputs: JSON, CSV, and custom formats for downstream use.

Module 3: Natural Language Processing for Intelligent Systems

• Text Representation & Understanding: Tokenization, embeddings, and vector stores.

• Chunking & Indexing Strategies for Knowledge Retrieval.

• Intro to Retrieval-Augmented Generation (RAG): Merging context with model intelligence.

• Hands-on Project: Build a context-aware chatbot using a retrieval pipeline.

Module 4: Optimizing Retrieval & Knowledge Integration

• Enhancing RAG Systems: Multi-step query resolution, relevance ranking.

• Reducing AI Hallucinations: Verification and fact-checking workflows.

• Fallback & Error Recovery Strategies.

• Performance Tuning & Evaluation Metrics for RAG.

Module 5: Designing AI Agents & Multi-Agent Workflows

• Agent Fundamentals: How autonomous AI agents operate.

• Tool-Using Agents: Connecting models to APIs, databases, and automation scripts.

• Collaborative Agents: Designing teams of agents to divide tasks and share information.

• Reasoning & Planning: Multi-step task execution and adaptive decision-making.

• Case Study: Multi-agent coordination in research automation.

Module 6: Building & Deploying AI Applications

• Creating AI-Powered APIs: Backend services using FastAPI or Flask.

• Interactive User Interfaces: Using Gradio or Streamlit for demos.

• Deployment Strategies: Cloud hosting, containerization, and CI/CD pipelines.

• Combining Agents, RAG, and UI into a Complete System.

• Monitoring & Maintenance: AI performance tracking and improvement cycles.

Module 7: Capstone Project – Multi-Agent AI in Action

• Design Brief: Build a fully functional AI system combining retrieval, reasoning, and multi-agent collaboration.

• Integration: External APIs, databases, and live data sources.

• Testing & Optimization: Ensure efficiency, scalability, and accuracy.

• Presentation: Showcase your solution to peers or potential employers.

Benefits:

• Learn practical AI development from prompts to deployment.

• Gain hands-on experience building retrieval-enhanced and multi-agent systems.

• Understand how to orchestrate multiple AI components into one cohesive application.

• Master deployment best practices for AI in production environments.

Who Should Enroll:

• AI developers and machine learning engineers.

• Software engineers integrating AI into workflows.

• Data scientists building intelligent applications.

• Innovators and entrepreneurs creating AI-powered products.

Enroll Today!

Master the technologies shaping tomorrow — become an AI & Generative AI professional ready for the future.