Loop Engineering is the practice of designing systems that automatically prompt AI agents, rather than prompting them manually. Boris Cherny runs hundreds of agents, Addy Osmani coined the term, and Blake Crosley identified verification cost as the real bottleneck — this article covers primary sources, the five building blocks, applicability boundaries, and criticisms.
Automatic prompt optimization (APO) has evolved from APE/OPRO to GEPA: replacing sparse rewards with linguistic reflection, winning over GRPO by ~6pp with 4-35x fewer rollouts. Meanwhile, tool descriptions are the overlooked prompt -- small wording changes can shift tool selection rates by 10x, and Anthropic's experiments show Claude self-rewriting tool descriptions outperforms human experts. These two lines are converging: eval-driven automatic optimization is eating hand-tuned prompts.
asgeirtj/system_prompts_leaks collects the raw system prompts of 40+ AI assistants, from GPT-5.5 and Claude Opus 4.7 to Gemini 3.1 Pro, with 40.3k stars, 461 commits, and an MIT license. The value isn't in obtaining secrets -- it's in turning vendors' implicit policies into comparable engineering material. What you should study is the design decisions, not the text itself.
Rewriting tool descriptions from soft suggestions to hard rules (whitelist + consequence explanation) eliminated the LLM's incorrect tool selection; adding skip_signal=True fixed vector store double-indexing.
A Skill is a folder with a SKILL.md. Three-layer progressive disclosure lets Claude load details only when needed, eliminating the need to re-explain preferences every conversation.
Every one of Claude Code's 45 tools uses a prompt() method that dynamically adjusts based on user type, feature flags, and system capabilities. Applying this pattern to a ReAct Agent, tool descriptions are dynamically generated along three dimensions: orchestrator model capability, locale, and available tools. Small models automatically get few-shot examples; large models save tokens.
AI engineering has gone through three phases: Prompt Engineering (write better instructions) → Context Engineering (feed the right information) → Harness Engineering (design the entire working environment). Each evolution doesn't replace the previous one — it operates at a higher level of abstraction.
Context Engineering is the core concept that replaced Prompt Engineering in 2025: the focus shifted from 'how to ask' to 'what information to provide.' Delivering the right information at the right time into the context window is more effective than upgrading to a stronger model. This post covers the definition, four key strategies, practical techniques, and common failure modes.
A complete study guide for Claude's official architect certification: five exam domains, six scenario types, common anti-patterns, and hands-on preparation strategies.
Good prompts aren't written in one go — they're iterated into existence. Start with the simplest prompt, test with real cases, classify error types, and make targeted fixes. This article covers the three-part System Prompt structure, reasoning framework selection, few-shot optimization, token budget management, and six common mistakes.
Search found the right documents, but the LLM's answers are still poor — often the problem lies in prompt design. System prompt structure, context formatting, and instruction placement all affect output quality.