AI Recipes

Humans have working, long-term, and procedural memory — an agent needs the same. In 2026 memory stopped being an optimization and became an architectural requirement: without it every session starts from scratch, and the user explains the same thing for the third time in a row.

Agents trust their first answer too much — and fail exactly where a human would reread and fix. Reflection forces the model to draft first, then critique, then revise — and quality jumps significantly.

In 2026 only 22% of people trust autonomous agents — and the EU AI Act explicitly requires human oversight for high-risk systems. An agent that knows when to pause and ask for approval at critical points is not a bug — it is a mandatory part of production architecture.

Most teams run one premium LLM on everything — from classification to architectural decisions. A smart router classifies the request first, then dispatches to a cheap/mid/premium model — cutting cost 30-80% without a quality drop.

87% of top models remain vulnerable to jailbreaks, EU AI Act fines reach €35M, and layered guardrails catch 95% of incidents collectively. We break down how to build defense at every seam of the pipeline: a single guardrail is no guardrail — just a feeling of one.

Everyone builds agents, almost nobody measures them. We break down why classical tests don't work here, how to assemble your first eval dataset in an hour, the difference between end-to-end and trajectory evaluation, and how not to fool yourself with LLM-as-judge.

The cheapest LLM optimization isn't a smaller model or a smarter RAG — it's a properly structured prefix cache. We break down how caching actually works, where to place cache points, why segment order matters more than content, and how to measure hit-rate so you don't fool yourself.

Classic RAG isn't the only way to build a personal knowledge base. We unpack Andrej Karpathy's idea: an LLM acting as a librarian that maintains a topical wiki from your raw notes. When it beats RAG, how the structure works, and what to do when it breaks.

A voice agent isn't 'a text bot plus TTS'. It's an STT → LLM → TTS chain that has to respond before the human starts talking again. We break down the latency budget, turn-taking, interruption handling, and three typical streaming-pipeline breakdowns.

Vision agents clicking through websites are the hottest automation trend of 2026. We break down when you actually need one (not always), the difference between DOM and vision approaches, why 'click and wait 2 seconds' almost always breaks, and which boundaries are mandatory before you ship to production.

Automatically analyzes review sentiment and generates a personalized response. We break down the core principle: one complex prompt is a trap, a chain of simple ones is power. We build a workflow with branching for cases when the AI needs to call a human.

MCP (Model Context Protocol) is like a USB interface for AI: a standard connector through which Claude plugs into any tool. We break down how to design and run your own MCP server — from choosing what tools to expose to making the first call from Claude Desktop.

Classic RAG is a brainless conveyor: query, retrieve, answer. Agentic RAG is when the agent decides whether to search, what to search for, and whether the results are good enough. We break down how to turn a linear pipeline into a decision loop — using LangGraph and ChromaDB.

Code review is a perfect task for an AI agent: it requires reading context, finding related files, and applying different criteria to different parts of code. We break down how to build an agent that reviews code like an experienced engineer — prioritizing findings, searching for context, and posting a structured report directly to GitHub.

Every company drowns in documents: invoices, contracts, requests, letters. AI can read them — but for this to work reliably, you need not one magic button, but a pipeline: first understand what the document is, then extract the needed data, then decide what to do with it. We break down how to build such a pipeline in n8n with the Claude API.

CrewAI is a framework where multiple AI agents work as a real team: each with a dedicated role, tools, and area of responsibility. We break down how to build a research crew with a coordinator and specialists — from role design to debugging your first real scenario.