AI Knowledge
How AI actually works
Not high-level overviews. Practical explanations of the components that make real AI systems work: Agents, RAG, LLMs, embeddings, and architecture — each with working examples.
2 of 8 topics published
What is an AI Agent?
How Agents differ from general AI, what components they need, and how they process real-world tasks step by step.
Agent Architecture: How It Actually Works
The full picture: how UI, Backend, RAG, Tools, and LLM connect — and why the loop between Backend and LLM is what makes an Agent an Agent.
RAG: Retrieval-Augmented Generation
How to give AI access to your own data by combining vector search with language models. The foundation of enterprise AI.
LLM Prompting Fundamentals
How to communicate effectively with language models: system prompts, few-shot examples, and chain-of-thought reasoning.
Embeddings & Vector Search
The mathematical foundation behind semantic search and how computers represent meaning as numbers in high-dimensional space.
Tool Use & Function Calling
How Agents interact with external systems by calling APIs, querying databases, and executing custom functions.
Agent Memory Systems
Short-term context windows, long-term vector storage, and how Agents learn from past interactions to improve over time.
Multi-Agent Architecture
How multiple specialized Agents collaborate, delegate tasks, and coordinate to solve problems no single Agent could handle alone.
New topics are added as I study and build. Each entry is written from the perspective of someone who has actually implemented it, not just read about it.