Learn AI, one deep dive at a time

Fundamentals

How Large Language Models Actually Work

Tokens, embeddings, attention and next-token prediction — the full journey from your prompt to the model's answer.

14 min read · Beginner Fundamentals

Embeddings Explained: How AI Understands Meaning

How AI turns text into vectors that capture meaning — and why that powers search, RAG and recommendations.

12 min read · Beginner Fundamentals

Prompt Engineering That Actually Works

Durable techniques — context, examples, structure, reasoning and output formats — with before-and-after examples.

15 min read · Beginner Tech Stack

The Modern AI Tech Stack, Layer by Layer

From GPUs and foundation models to vector databases, orchestration and observability — what each layer does and when you need it.

16 min read · Intermediate Tech Stack

Choosing a Vector Database

How vector search works under the hood (HNSW, IVF, quantization), how the main options compare, and how to pick one.

14 min read · Intermediate Development

Build a RAG App From Scratch

Make an AI answer questions about your own documents: chunking, embeddings, retrieval and grounded generation, with working Python.

18 min read · Intermediate Development

Fine-Tuning vs. RAG vs. Prompting

What each approach really changes, what it costs, and a decision framework for choosing the right one.

13 min read · Intermediate Development

Building AI Agents with Tool Use

Tool use, the agent loop, tool design and safety guardrails — with a working agent built from scratch in Python.

17 min read · Intermediate Development

Evaluating LLM Applications

Test sets, code checks, LLM-as-judge, RAG and agent metrics — turn AI quality from guesswork into engineering.

15 min read · Intermediate Fundamentals

Responsible AI: Risks, Safety and Good Practice

Hallucinations, bias, privacy, security and misuse — the real risks, and the practical habits that prevent most problems.

15 min read · Beginner Fundamentals

Multimodal AI: Images, Audio and Beyond

How AI sees images, hears speech and creates pictures — vision encoders, CLIP, diffusion — and where it still stumbles.

14 min read · Beginner Tech Stack

Running AI Models Locally

Run open-weight models privately on your own computer: hardware, quantization, and setup with Ollama, LM Studio and llama.cpp.

14 min read · Beginner–Intermediate Fundamentals

The AI Glossary: 60+ Terms Explained

Plain-English definitions of the AI terms you'll meet — from agents and attention to tokens and transformers.

Reference · All levels