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Concepts & Fundamentals

The grounding everything builds on — LLMs, tokens, agents, RAG.

  1. What Is an LLM, Really?Intro7 min readLarge language models are not search engines or databases - they learn patterns from text and predict what comes next. Here is how that simple loop produces something that genuinely reasons.
  2. Tokens, Context Windows & Why They MatterIntro7 min readTokens are the unit of measure every AI model charges, limits, and thinks in. Learn what they are, how context windows cap what the model can "see," and the practical rules that shape every prompt you write.
  3. Prompting That Actually WorksIntro7 min readMaster the four mechanics of a good AI prompt - role, context, format, and examples - and learn why vague input reliably produces garbage output. Concrete before/after examples included.
  4. What Is an Agent?Core8 min readAI agents go beyond answering questions - they take actions, use tools, and run in loops until a goal is met. Learn how tools, memory, and the agent loop work, and where agents break down in practice.
  5. Embeddings and Vectors Without the MathCore7 min readEmbeddings turn text, images, and video into lists of numbers that capture meaning - powering semantic search, recommendations, and RAG. Here's how they work and how to build with them today.
  6. RAG: Giving Your AI Real KnowledgeCore8 min readRAG (Retrieval-Augmented Generation) connects an AI model to your own data at inference time, solving the training-cutoff problem without retraining the model. Learn how it works and how to build it.
  7. Hallucinations: Why AI Lies and What to Do About ItCore9 min readAI hallucinations happen when a model generates confident, fluent text that is factually wrong. Learn why it happens mechanically, how to spot it, and the practical patterns that actually reduce it.
  8. Fine-Tuning vs Prompting vs RAG: The Tradeoff MapDeep8 min readPrompting, RAG, and fine-tuning solve different problems. Learn the cost, capability, and timing tradeoffs - and the decision framework that tells you which one (or which combination) to reach for first.
  9. Model vs Product: What You're Paying ForDeep9 min readThe stack from weights to wrapper: base models, APIs, products built on top, and how to read the AI landscape so you spend money in the right place.