Prompt Engineering

12 articles on prompt engineering.

How to Reduce AI API Costs Without Sacrificing Accuracy

Learn how to reduce AI API costs with cache audits, batch jobs, routing, prompt trimming, evaluation gates, and a realistic self-hosting model.

Claude Code prompt optimization becomes measurable with autoresearch, frozen evaluators, holdout tasks, and cost controls for coding agents.

AI agent regression testing helps teams catch hallucinations, policy drift, and broken multi-turn flows before prompt changes reach customers.

Prompt caching for AI APIs can cut repeated-context costs by up to 90%. Learn what to cache, avoid cache misses, and measure ROI.

A local AI chat compressor can cut context costs and speed LLM switching, but preserving intent requires more than shorter prompts.

Use AI website design prompts that specify references, layout, and assets to escape generic hero sections and ship landing pages with intent.

Learn progressive context shaping for long-running AI agents: portable state, decision logs, checkpoints, and safer human oversight.

Minimal AI agent harnesses can reduce cost and preserve model judgment. Here’s what Pi, Oh My Pi, and recent benchmarks reveal.

Learn how to build AI agent skills that activate reliably, stay auditable, avoid context bloat, and improve real-world agent workflows.

Claude Code context engineering is shifting from massive rule files to lean, on-demand guidance. Learn what to remove, retain, and test safely.

An AI model harness can quietly cause agent failures. Use this six-step audit to reduce prompt bloat, preserve safeguards, and improve reliability.

Build a Claude content calendar that turns a lengthy planning meeting into a reviewable, channel-ready B2B SaaS campaign draft in minutes.