Articles
Chain-of-Thought and Advanced Reasoning Prompting Techniques
How chain-of-thought, self-consistency, and decomposition prompting work — and when they're obsolete on reasoning models.
Multi-Agent Systems Explained
A team of agents is a scaling tool, not an upgrade. The two situations where multiple agents beat one, when a single agent wins, and how to decide.
RAG vs Tools vs Long Context
Three ways to give a model knowledge — and the obvious one is often wrong. When to use long context, RAG, or tools, plus the 2026 shift to agentic retrieval.
How to Design Tools Your AI Agent Can Actually Use
When an agent misbehaves, the tools are usually the problem, not the model. Six rules for tool names, descriptions, schemas, results, and permissions that make a capable model a reliable system.
What Is Context Engineering
The skill that superseded prompt engineering. Treat the context window as a budget, not a bucket — and manage it with four moves: write, select, compress, isolate.
System Prompts vs User Prompts
Almost every AI app sends the model two kinds of instruction. What belongs in the system prompt vs the user prompt, why the split matters for consistency and security, and the mistakes to avoid.
How to Write Evals for Your AI Agent
The step everyone skips is the one that separates a demo from a product. How to evaluate an agent's whole trajectory — task completion, tool use, and reasoning — with deterministic checks and LLM-as-judge.
Prompt Caching: How to Cut LLM Cost and Latency
Prompt caching is close to free money: reuse your stable prompt prefix to cut cost up to ~90% and latency up to ~80%, at the same quality. How it works and the one rule that unlocks it.
How to Prompt Reasoning Models (Without Getting in Their Way)
The advice that made older models better makes reasoning models worse. Why 'think step by step' now backfires, and how to prompt thinking models by getting out of their way.
Few-Shot vs Zero-Shot: When Examples Actually Help
Examples are a precision tool, not a default. When few-shot prompting sharpens results, when it quietly backfires on reasoning models, and a simple rule for choosing.