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Learn AI from scratch — what RAG, agents, prompts, fine-tuning, alignment, and context windows actually mean; how to pick a tool; practical use cases; advanced techniques.
Debug a multi-step agent that's behaving weirdly
Step 4 went sideways and you can't figure out why. Here's the systematic playbook.
Hybrid search (BM25 + vector) for RAG systems
Pure vector search misses keywords. Pure keyword search misses semantics. Combine them — here's the recipe.
Structured outputs from LLMs: tool use, JSON mode, schemas
Three ways to make a model emit valid JSON, when each one wins, and the failure modes that surprise you in production.
Speculative decoding: how to make inference 2-3× faster
A small model proposes tokens. A big model verifies them in parallel. Same output, dramatically less latency.
LLM routing: route easy queries to cheap models
Most queries don't need Opus. A simple router cuts costs 60-80% with negligible quality loss — if you build it right.
How to evaluate LLM output quality at scale
Three eval flavors that actually scale — golden datasets, LLM-as-judge, and online metrics — plus how to know which to use when.
Agent memory strategies: from session to long-term
Four memory layers, when each matters, and the tradeoffs between fancy frameworks and 50 lines of your own code.
When fine-tuning beats prompt engineering (and when it doesn't)
Most teams jump to fine-tuning too early. The decision tree, the actual numbers, and the order to try things in.
What is RAG (Retrieval-Augmented Generation)? A Practical Guide
RAG lets an LLM answer using your private documents instead of guessing. Here's how it actually works, when it's worth the cost, and when fine-tuning or long context is a better choice.
What is AI in 2026? A 5-minute primer for non-technical readers
AI in 2026 isn't one thing. It's chatbots that write code, models that watch your screen, and agents that book your flights — here's how to make sense of it all.
What is a Large Language Model (LLM)? A plain-English explainer
An LLM doesn't think — it predicts the next word. That single fact explains both why ChatGPT feels magical and why it confidently makes things up.
What is a prompt? And why prompt quality decides everything
A prompt is just the text you send to an LLM. But the difference between a vague prompt and a specific one is the difference between a useless answer and a great one.
What is a context window? The hidden ceiling of every LLM
The context window is the amount of text the model can see at once. Bigger windows enabled the long-document era, but they don't solve every problem — and they cost real money.
What is a token, in LLM-speak? And why it matters for your bill
Tokens are the chunks an LLM actually sees. They're not words, they're not characters — and they're how every API decides what to charge you.
What is an AI agent? And how is it different from a chatbot?
An agent is an LLM that can take actions: click links, run code, query APIs, then check its own work and try again. The 'try again' part is what makes it both powerful and unstable.
What is fine-tuning, and when do you actually need it?
Fine-tuning trains a model on your data. It sounds like the obvious answer for any custom AI feature — but in 2026, it's almost never the right first move.
What is vibe coding? And how to do it without ending up with junk
Vibe coding is letting AI write the code while you steer the product. With Cursor, Lovable, and v0 it's now a real workflow — but only if you treat the AI like a junior, not a genie.
What is MCP (Model Context Protocol)? The USB-C of AI
MCP is an open standard that lets any AI assistant talk to any tool — your filesystem, GitHub, Notion, your own database — through one protocol instead of one custom integration per pair.
What is an API key, and how to use one without leaking it
An API key is your password to a paid service like OpenAI or Anthropic. Leaking it means strangers spend your money — and almost everyone leaks one at least once.
What is an embedding? Turning meaning into math
An embedding is a list of numbers that represents what a piece of text means. Similar meanings = similar numbers. It's the math behind semantic search, RAG, and recommendations.
What is a vector database, and do you actually need one?
A vector database stores embeddings and finds similar ones fast. For most starting RAG apps, your existing Postgres with pgvector is enough — and a dedicated vector DB might be over-engineering.
What is a multimodal model? When AI can see, hear, and read at once
Multimodal means the same model handles text, images, audio, and video. Modern Claude, GPT-5, and Gemini will read your screenshot like text — and that changes what you can build.
What are reasoning models? o3, DeepSeek R1, and the 'think before you speak' shift
Reasoning models pause and 'think' before answering — sometimes for minutes. They're better at math and code, worse for casual chat, and they cost more. Use them where it counts.
What is tool use (function calling)? How LLMs reach beyond text
Tool use lets an LLM call functions you provide — search the web, query a DB, send an email. It's the bridge from chat to action, and the foundation of every modern AI agent.