Blog · AI & LLMs
Plain-English guides to how LLMs work, prompt engineering, RAG, AI agents and machine learning — for developers and business owners.
6 min read
Tools like Claude Code, Cursor and GitHub Copilot can now plan, edit files, run tests and open pull requests. Here’s how to use AI coding agents on production code without losing quality, security or understanding — with concrete team guidelines.
6 min read
A demo that works on ten examples is not a product. Here’s a practical production checklist for AI features — evaluation sets, guardrails, fallbacks, cost and latency control, monitoring and rollout — so your LLM feature stays reliable after launch.
6 min read
The best AI features aren’t built on clever prompts — they’re built on giving the model exactly the right information at the right moment. Here’s what context engineering is, why it matters more as models get smarter, and production guidelines for doing it well.
9 min read
Tokens, embeddings, attention, training and sampling — what really happens between your prompt and the model’s answer, explained without the hype and without a math degree.
7 min read
Forget magic phrases. Good prompts are clear briefs — context, a precise task, examples, structure and a way to check the result. Here are the techniques that consistently make AI output better.
5 min read
Embeddings turn text, images and products into lists of numbers that capture meaning — the technology behind semantic search, recommendations and RAG. Here’s how they work and how to use them in a real app.
7 min read
Retrieval-augmented generation lets a language model answer questions using your documents instead of its memory. Here’s how the pipeline works — chunking, embeddings, vector search, prompting — and how to build one that’s actually reliable.
7 min read
An AI agent is a language model in a loop with tools. Here’s how tool calling works, what the Model Context Protocol (MCP) standardizes, and how to build agents that are useful without being dangerous.
5 min read
The best way to understand deep learning is to build a small network by hand — forward pass, loss, backpropagation and gradient descent — and then rebuild it the idiomatic PyTorch way. A beginner-friendly, code-first walkthrough.
6 min read
Describing what you want and letting AI write the code has gone from joke to everyday workflow. Here’s an honest look at what vibe coding does well, the risks it hides, and a practical way to use it without shipping a mess.
6 min read
AI isn’t replacing developers or businesses that adapt — it’s raising the bar for everyone else. Here’s why learning AI matters now, what skills are worth your time, and a realistic learning path that doesn’t require a PhD.