Most AI content today is either:
- too academic
- too mathematical
- too shallow
- too “black-box”
So beginners get confused.
Developers get overwhelmed.
PMs and designers feel lost.
And even experienced engineers struggle to understand how AI systems actually work.
That’s why I’m starting a new series:
🔥 “AI Engineering for Everyone — Simple Explanations of Hard Concepts”
A series dedicated to breaking down complex AI ideas using:
- simple language
- clear diagrams
- real analogies
- no excessive math
- beginner-friendly logic
If you can understand a concept clearly — you can build with it confidently.
📌 What This Series Will Cover
1️⃣ What Actually Happens Inside an LLM
A simple breakdown of how models reason, generate, and predict.
2️⃣ Embeddings…
Most AI content today is either:
- too academic
- too mathematical
- too shallow
- too “black-box”
So beginners get confused.
Developers get overwhelmed.
PMs and designers feel lost.
And even experienced engineers struggle to understand how AI systems actually work.
That’s why I’m starting a new series:
🔥 “AI Engineering for Everyone — Simple Explanations of Hard Concepts”
A series dedicated to breaking down complex AI ideas using:
- simple language
- clear diagrams
- real analogies
- no excessive math
- beginner-friendly logic
If you can understand a concept clearly — you can build with it confidently.
📌 What This Series Will Cover
1️⃣ What Actually Happens Inside an LLM
A simple breakdown of how models reason, generate, and predict.
2️⃣ Embeddings Explained in One Diagram
What embeddings truly represent — the foundation of semantic search.
3️⃣ How RAG Works (With a Real-Life Analogy)
Why retrieval improves accuracy and how the architecture works.
4️⃣ Why Models Hallucinate (And How to Fix It)
The UNKNOWN root causes of hallucinations + engineering solutions.
5️⃣ Tokenization Explained for Humans
How models “see” text and why tokenization matters.
6️⃣ What Vector Databases Really Do
How they store, index, and retrieve embeddings efficiently.
7️⃣ What Makes AI “Think” Step-by-Step
Chain-of-thought, planning, reasoning — simplified.
8️⃣ Why Retrieval Is More Important Than the Model
How retrieval quality now matters more than model size.
9️⃣ How Memory Works in AI Systems
Short-term, long-term, episodic, and summary memory explained.
🔟 How AI Agents Decide What To Do Next
A clean explanation of agent planning, loops, and decision-making.
🎯 Why This Series Matters
AI is no longer a niche skill. It’s becoming essential for:
- engineers
- data scientists
- designers
- PMs
- founders
- students
- teams building AI products
Yet most people never learn the intuitive foundations behind:
- LLM internals
- embeddings
- vector search
- RAG
- hallucination mechanics
- memory
- reasoning
- agents
This series makes AI clear, practical, and understandable — without losing depth.
💬 Want Part 1?
If you want Part 1: “What Actually Happens Inside an LLM”, comment below — I’ll publish it next with diagrams and a DEV-ready version.
Stay tuned. This series is going to simplify AI for thousands.