Monish Gori — AI Student & Full Stack Developer | Mumbai University, Mumbai

	I'm Monish Gori — an AI student and full stack developer based in Mumbai, currently learning through Mumbai University while building my foundation in Generative AI, Prompt Engineering, and Agentic AI. I come from a full stack background, so I naturally get excited about the point where software, systems thinking, and AI start meeting in a practical way. More than just using tools, I want to understand how these models work, why they behave the way they do, where they fail, and how they can be used inside real products and workflows. I've started organizing everything I learn into this public knowledge base so my notes stay structured, connected, and useful not just for revision, but for actually growing as a builder over time.
LinkedIn	monish-gori-67676125b
GitHub	monishgori
Portfolio	resume-portfolio-woad.vercel.app
Digital Garden	Open this website

About This Site

This digital garden is my public learning space for AI.

Here I am organizing:

module-wise study notes
prompt engineering concepts
LLM fundamentals
mental models for using AI correctly
connected ideas that I can revise later quickly

Why I made this

I wanted one place where my AI learning feels organized, personal, and actually useful to return to.

About This Course

This website follows my learning path through foundational AI concepts, LLM behavior, and prompt engineering. Instead of only focusing on tools, I want to understand what is happening underneath the surface, why models behave the way they do, and how to use them with more clarity.

The purpose is not to collect fancy words. The purpose is to build understanding that can later help in real projects, real workflows, and better technical thinking.

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

how AI evolved from rules to modern generative systems
what actually happens inside an LLM
why AI gives strong answers sometimes and wrong answers other times
how prompts shape output
how AI usage can grow into repeatable systems and 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

Module 1 — AI Landscape & Transformation

Notes	Topic
M1-A - The Intelligence Stack	AI vs ML vs Deep Learning vs Generative AI, and how the stack evolved from rules to agents
M1-B - Prompting as a Skill	Prompting as a specification skill, not just asking questions
M1-C - Where AI Actually Matters	Where AI creates real leverage and where human judgment still matters

Module 2 — LLM Fundamentals

Notes	Topic
M2-A — What is Happening Inside AI	What is happening behind the scenes inside modern AI systems
M2-B — How AI Generates Answers	How prompts, tokens, and prediction combine to generate output
M2-C — Why AI Makes Mistakes	Hallucinations, confident errors, and context limitations
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 clearly

Module 3 — Advance Prompt Engineering

Notes	Topic
M3-A - What is a Prompt?	What a prompt really is and how it shapes model behavior
M3-B - Role-Based Prompting	Using roles to steer the model toward the right style and response type
M3-C - Prompt Chaining	Designing multi-step prompt workflows for stronger results

Assignment 1 Prompt designer Extenstion
(Assignment 2 ) Linkedin evaluation with engineered prompt