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FROM FIRST PROMPT TO PRODUCTION

Understand AI.
Build with confidence.

Learn generative AI one concept at a time, with clear examples you can follow and notes you can return to.

66 lessons10 modulesVideo + written notes
Explore the curriculum ↓
COURSE HIGHLIGHT · 57 SECONDS

A prompt becomes an email.

A simple example from Lesson 2: see how an instruction guides a generated response.

Press Play to watch. English captions are available in the player.

See the matching notes.

Three real pages from Lesson 2’s handwritten PDF. Select a page to read it.

Free course highlight and sample notes. Full lesson videos and PDFs are available through purchased course access.

Lesson 2 · Sample page 1 of 3

The leave-request example · Page 1 from the 10-page lesson PDF

Actual Lesson 2 PDF page 1: The leave-request example

Sample excerpt · The complete PDF is attached to Lesson 2 in the purchased course.

01 / WATCH

See the idea take shape.

Follow each concept through an explained example in the lesson viewer.

02 / READ

Return to the explanation.

Read the topic notes directly below the video, including definitions and key takeaways.

03 / REVISE

Keep the whole lesson close.

Handwritten PDFs cover the narration with detailed diagrams, examples and worked steps.

YOUR LEARNING PATH

Ten modules. One connected course.

01

AI Foundations & Responsible Use

5 lessons
  1. 01
    AI vs ML vs Deep Learning
  2. 02
    Introduction to Generative AI (GenAI)
  3. 03
    Open-source vs Closed-source AI Models
  4. 04
    GenAI Platforms Overview (OpenAI, Anthropic, Mistral, Stable Diffusion, Midjourney)
  5. 05
    Ethics & Responsible AI Usage
02

Prompt Engineering & Model APIs

9 lessons
  1. 06
    Prompt Anatomy (Role, Instruction, Input, Output)
  2. 07
    Prompting Techniques (Zero-shot, Few-shot, Role-based Prompting)
  3. 08
    Advanced Prompting (CoT, ToT, ReAct, Self-Consistency)
  4. 09
    Handling AI Responses (JSON parsing, streaming, batch outputs, error handling)
  5. 10
    AI Guardrails & Output Validation
  6. 11
    Conversation Memory & Context Management
  7. 12
    Working with OpenAI/Gemini APIs (Chat, Embeddings, Function Calling)
  8. 13
    API Security, Rate Limits & Key Management
  9. 14
    Debugging & Evaluating GenAI Outputs (Accuracy, Relevance, Reliability)
03

Text Processing & Embeddings

5 lessons
  1. 15
    Text Preprocessing & Tokenization
  2. 16
    Text Representation & Feature Engineering
  3. 17
    Named Entity Recognition (NER) & POS Tagging
  4. 18
    Embeddings, Vectorization & Semantic Search
  5. 19
    Chunking Strategies for LLM Applications
04

Building RAG Pipelines

7 lessons
  1. 20
    RAG Fundamentals & Architecture
  2. 21
    Retrieval + Generation Workflow in RAG
  3. 22
    Data Handling, Chunking & Indexing
  4. 23
    Vector Databases & Vector Stores
  5. 24
    Dense vs Sparse Retrieval & Hybrid Search
  6. 25
    Working with Text/Image Embeddings in Vector DBs
  7. 26
    Building a Simple RAG Pipeline with LlamaIndex
05

Advanced RAG & Evaluation

6 lessons
  1. 27
    Query Rewriting, Multi-hop & Multi-query Retrieval
  2. 28
    Reducing Hallucinations & Context Optimization
  3. 29
    Advanced RAG Techniques (Self-RAG, Corrective RAG, Adaptive RAG, Contextual Retrieval, LOTR)
  4. 30
    Why Basic RAG Fails & Advanced Improvement Strategies
  5. 31
    RAG Evaluation & Fallback Mechanisms
  6. 32
    RAGAS Metrics (Faithfulness, Relevance, Context Recall & Precision)
06

AI Agents & Frameworks

10 lessons
  1. 33
    Introduction to AI Agents & Agent Architectures
  2. 34
    Types of AI Agents (Reflex, Goal-based, Utility-based, Tool-based)
  3. 35
    Reactive Agents & Agent Design Patterns
  4. 36
    Tool Calling, Execution Flows & Prompting Strategies
  5. 37
    Multi-Agent Systems (MAS) & Collaborative AI
  6. 38
    Agent Coordination, Negotiation & Consensus Mechanisms
  7. 39
    Popular Agent Frameworks (LangChain, LlamaIndex, AutoGen, CrewAI, LangGraph)
  8. 40
    ReAct Agents & Tool-Using Agents
  9. 41
    LangGraph for Stateful Agent Orchestration
  10. 42
    Framework Comparison: Choosing the Right Agent Stack
07

Advanced Agents & Agentic RAG

10 lessons
  1. 43
    Planning, Reasoning & Tool Orchestration in Agents
  2. 44
    Hierarchical & Collaborative Multi-Agent Patterns
  3. 45
    Goal-Oriented Agent System Design
  4. 46
    Multi-Agent Reinforcement Learning (MARL) Basics
  5. 47
    MCP (Model Context Protocol): Servers, Clients, Tools & Resources
  6. 48
    A2A (Agent-to-Agent) Communication Protocols
  7. 49
    Agentic RAG: Autonomous Retrieval + Reasoning Pipelines
  8. 50
    Single-Agent vs Multi-Agent Agentic RAG
  9. 51
    Agent Debugging, Tracing & Monitoring with LangSmith
  10. 52
    Latency, Performance & Cost Optimization for AI Agents
08

API Development with FastAPI

3 lessons
  1. 53
    Web API Fundamentals (HTTP Methods: GET, POST, PUT, DELETE)
  2. 54
    Building APIs with FastAPI & Pydantic
  3. 55
    API Documentation with Swagger/OpenAPI
09

Deployment, Containers & Scaling

4 lessons
  1. 56
    Deploying AI Apps (Render, Railway, Replit, Hugging Face Spaces)
  2. 57
    Dockerization for AI Applications
  3. 58
    CI/CD Pipelines with GitHub Actions
  4. 59
    Kubernetes (K8s) Basics for GenAI Scaling
10

AI Application Architecture & Integration

7 lessons
  1. 60
    GenAI System Architecture Review (Agents, RAG, APIs)
  2. 61
    Designing Modular AI Systems & Microservices
  3. 62
    FastAPI for LLM Orchestration & LangChain Servers
  4. 63
    Frontend Integration with Gradio & Streamlit
  5. 64
    UI/UX Basics for LLM Applications
  6. 65
    API Security, Environment Variables & Rate Limiting
  7. 66
    Monitoring, Logging & Error Handling