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I' m Vighnesh Pawar -I am actively expanding my skillset as an AI learner. I am focused on mastering artificial intelligence concepts, tools, and practical applications to drive efficiency and innovation. By developing this expertise, I aim to leverage advanced technologies to optimize our workflows, support data-driven decision-making, and contribute strategic value to our team’s upcoming projects. |
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| https://www.linkedin.com/in/vighnesh-pawar-742377407/ | |
| GitHub | https://github.com/vighneshPawar-alt |
| Vercel site | https://agentic-ai-program.vercel.app/ |
| Digital garden | open this website |
About this course
I use this website to track my journey through foundational AI, LLM behavior, and prompt engineering. Instead of just learning how to use current tools, I study what happens underneath the surface.
My goal is to understand exactly why models behave the way they do so I can use them with absolute clarity. I focus on building deep, clear understanding instead of collecting fancy buzzwords.
Every note here is meant to be practical and directly useful for real-world projects and everyday workflows. I treat this digital garden as a tool to sharpen my technical thinking and build long-term value.
What I'm Building Here
Through these notes, I am building:
- a structured second brain for AI
- a cleaner revision system for every module
- a personal knowledge base I can keep expanding
- a foundation for future AI projects and workflows
What I Want to Understand Properly
- The Evolution: How AI changed from rigid, rule-based coding into flexible generative models.
- The Mechanics: What actually happens inside a large language model during data processing.
- The Reliability: Why models give brilliant answers sometimes and completely fail other times.
- The Control: How specific prompt inputs directly shape and change the final output.
- The Scaling: How to turn simple AI prompts into reliable, repeatable real-world products.
How to Use This Digital Garden
You can explore the notes by module, or open the full INDEX
for a structured overview.
If you are also learning AI, this site is best read like a connected notebook:
- start from the early modules
- follow the note links
- revisit topics as they connect with later ones
Notes
Website notes directory for my Generative AI & Agentic AI learning project.
Module 1 — AI Landscape & Transformation
| Notes | Topic |
|---|---|
| M1-A - The Intelligence Stack | AI vs ML vs Deep Learning vs Generative AI |
| M1-B - Prompting as a Skill | Prompting is not "talking nicely to AI." It is the skill of specifying behavior inside a probabilistic system. |
| M1-C - Where AI Actually Matters | Where AI Actually Matters |
Module 2 — LLM Fundamentals
| Notes | Topic |
|---|---|
| M2-A — What is Happening Inside AI | How ai works behind the scenes |
| M2-B — How AI Generates Answers | what to do to get perfect response from AI |
| M2-C — Why AI Makes Mistakes | How and why AI makes mistakes |
| M2-D — How to Use AI Correctly | Safe habits, good prompting, and correct AI usage |
| M2-E — Your Final Mental Model of AI | Final summary of how to think about AI correctly |
Module 3 — Advance Prompt Engineering
| Notes | Topic |
|---|---|
| M3-A - What is a Prompt | What a prompt really is and how it controls model behavior |
| M3-B - Role-Based Prompting | Using roles to steer the model toward the right style and behavior |
| M3-C - Prompt Chaining | Designing multi-step prompt workflows for better output |