标签 · Prompt Engineering — Glean

Recent picks

3picks · chronological

07-14

The 'Caveman Skill' That Claims 65% Token Savings Actually Saves Only 8.5%

This article analyzes the recent trend of 'caveman skills' (such as the Caveman project) that prompt AI coding tools to output minimal language to save tokens. The author points out that the claimed 65% token savings comes from chat scenarios, whereas in agentic programming tasks, tool calls and system prompts dominate token usage. A controlled test by JetBrains (86 tasks, 240 trials) showed that even with forced activation, output token savings were only 8.5%, and in practice the savings are even smaller due to conditional activation. The article also discusses the cost of brevity: loss of information leads to more developer follow-ups and agent rework. The author argues that true cost optimization comes from context management (e.g., prompt caching) and reducing unnecessary tool calls, not from compressing output.

x.com · 2 min · AI Engineering · Claude Code · Prompt Engineering

06-26

Loop Engineering for Beginners: From Concept to Minimal Loop

This article is a beginner-friendly tutorial that demystifies Loop Engineering: it's not a buzzword but a structured framework for human-AI collaboration, formalizing repetitive actions like goal-setting, stepwise execution, quality checks, feedback loops, and stop conditions. It clearly distinguishes between ordinary prompting (one-shot) and looping (sustained closure), then walks through a minimal viable example—building a personal knowledge base using the LLM Wiki approach. The author stresses that the 'check' step is the heart of any loop, and that without inspection criteria, automation just generates garbage. It also warns against common beginner mistakes such as mistaking long prompts for loops, overly ambitious goals, vague standards, lack of human sign-off, and granting excessive permissions. The article is instructional rather than deep, but it provides a ready-to-use template and the seven-element checklist (goal, input, execute, check, feedback, record, stop). It's best suited for engineers new to agentic workflows who want a structured starting point.

x.com · 7 min · Agent Workflow · Beginner Tutorial · LLM Wiki

06-21

Ponytail: Lazy Senior Dev Inside Your AI Agent, Cuts Code Bloat by ~54%

Ponytail is a rule plugin for 14+ AI coding agents (Claude Code, Codex, Copilot CLI, etc.) that injects a lazy-senior-dev mindset. Before generating code, it forces the agent to climb a ladder: does this need to exist? Can the standard library or native platform feature do it? Can it be one line? Only then writes the minimum viable solution. Benchmarked on real Claude Code sessions editing a real FastAPI + React repository across 12 feature tickets, it cuts lines of code by 54% (mean), tokens by 22%, cost by 20%, and time by 27% while keeping 100% safety on validation, error handling, security, and accessibility. Ideal for developers tired of AI bloat and over-engineering.

github.com · 12 min · Agents · AI Engineering · Code Generation