标签 · Prompt Engineering — Glean

Recent picks

12picks · chronological

08-31

Why Claude Code outputs are moving from Markdown to HTML

The author argues that Markdown is too restrictive as an agent output format and documents his shift to HTML in Claude Code. HTML increases information density (tables, SVG, CSS, JS), makes long specs readable, is easier to share as a link, and supports two-way interaction via sliders or buttons. Use cases include exploration/specs, PR review with annotated diffs, design prototypes, research reports, and throwaway HTML editors that export 'copy as prompt' back to Claude Code. Trade-offs are acknowledged: generation is 2–4x slower, and HTML diffs make version control review harder. Full of concrete prompt examples for engineers working with agent workflows.

x.com · 14 min · Agent Engineering · Claude Code · Developer Tools

08-31

The new rules of context engineering for Claude 5 generation models

Thariq Shihipar, a member of technical staff at Anthropic, explains how context engineering for Claude Code has changed with Claude 5-generation models. The old playbook—hard rules, examples, everything upfront, repeated instructions, CLAUDE.md as memory, and simple markdown specs—is now partly myth. New guidance: let the model use judgement (the system prompt now says 'match the surrounding code' instead of banning multiline comments); design expressive tool interfaces instead of stuffing examples; adopt progressive disclosure by loading verification and review skills on demand and letting tools defer their definitions until ToolSearch finds them. CLAUDE.md should stay lightweight, holding repo-specific gotchas, while skills encode team-specific opinions. Specs can become rich references: HTML mockups, test suites, code to port, or rubrics with verifier agents. Anthropic also ships `claude doctor` to auto-simplify contexts. No benchmarks are included, and the article is vendor-authored, but it offers immediately actionable patterns for agent harness and Claude Code users.

claude.com · 7 min · Agent Engineering · Agent Skills · Claude Code

08-24

Anthropic's internal ELI5 skill: explain like I'm five, with HTML visuals

Anthropic has been using an ELI5 Skill internally, triggered by /eli5 <topic>. It instructs the agent to explain a subject to a complete newcomer: avoid jargon, favor one big picture over dense text, and render complex ideas through an HTML artifact. The real payoff, as the post argues, is not simpler wording but forced knowledge restructuring — the agent must establish a mental model before diving into details and edge cases. Borrowed from Reddit's 'explain like I'm five' culture, this is a reusable output constraint for agent prompts, independent of any specific model, and easy to replicate in tools that support slash commands or custom skills. Useful for engineers tuning Claude or other agent output formats.

x.com · 1 min · Agents · AI Engineering · Prompt Engineering

08-13

Grok 4.6 Field Guide: Verification Loops Beat Long Prompts

The author used Grok 4.6 as a daily driver for weeks across coding and knowledge work, running side-by-side comparisons against 4.5 with identical prompts. Key finding: short prompts plus a clear preference match two-page specs, while adding a single sentence demanding post-implementation verification and iteration had the highest leverage. 4.6 performs steadily on browser automation, visual QA, inbox triage, and editing a real Excalidraw codebase, but 3D and video work still need human oversight because a screenshot cannot confirm time-based behavior. Probably written by an xAI team member, so treat the launch framing skeptically; the methodology and prompt examples are useful for engineers working with AI coding agents.

x.com · 10 min · Agent Engineering · LLM · Prompt Engineering

08-11

What Is an AI Engineer?

A concise role introduction to AI engineering, drawing on Latent Space's 'The Rise of the AI Engineer.' It draws the line at the API boundary: AI Engineers orchestrate models to build applications, while ML Engineers build the model APIs themselves. The post argues newcomers don't need linear algebra or pretraining experience; instead they need strong software fundamentals, evaluation frameworks, and feedback loops. It also distinguishes AI Engineers from AI-assisted developers who merely use tools like Copilot. Web developers are presented as well suited for the transition, and TypeScript is called a fast-growing fit. The article is accessible but conceptual, with a promotional block for the author's AI Hero skills system.

www.aihero.dev · 4 min · AI Engineering · Career Advice · LLM

08-09

17 Techniques for Improving Your LLM-Powered App

This guide walks through 17 techniques for improving LLM-powered applications, deliberately ordered from cheapest to most expensive on what the author calls the Staircase of Complexity Hell. It starts with prompt tweaks — role prompting, XML tags, structured outputs, chain-of-thought, multishot, temperature, and tool calling — before moving to system-level patterns: RAG, chunking, agentic loops, parallelization, evaluator-optimizer, LLM routers, and fine-tuning. The core advice: work down the staircase only after simpler options are exhausted. Concrete evidence includes Claude 4.6 returning 400 on prefilling, parallel processing cutting 10-document analysis from ~10s to 2-3s, routers bypassing the common ~30-tool limit per model, and fine-tuned smaller models beating larger ones on specific tasks. Each section names real trade-offs, such as CoT increasing latency and agentic loops adding decision overhead. Useful as a practical checklist for engineers iterating on LLM systems.

www.aihero.dev · 23 min · Agent Engineering · AI Engineering · LLM

08-04

Claude Code regression post-mortem: root causes and fixes

Anthropic published a post-mortem for recent reports that Claude Code felt less intelligent. They confirm the API was unaffected and trace the problem to three separate changes. First, the default reasoning effort in Claude Code was lowered from high to medium on March 4 to reduce latency; users pushed back and it was reverted on April 7, with Opus 4.7 now defaulting to xhigh. Second, a caching optimization using the clear_thinking_20251015 header was meant to clear stale reasoning only once after an hour of idle, but a bug made it clear reasoning on every subsequent turn, causing forgetfulness, repetition, and odd tool calls. This also produced continuous cache misses, draining usage limits faster. It was fixed on April 10. Third, a system prompt instruction limited text between tool calls to 25 words and final responses to 100 words; ablations showed a 3% drop on evals for both Opus 4.6 and 4.7, and it was reverted on April 20. Anthropic is resetting usage limits, adding per-model evals for prompt changes, improving internal dogfooding with the public build, and enhancing Code Review tooling. A useful case study in agent-product debugging and the cost of silent tradeoffs.

www.anthropic.com · 11 min · Agent Engineering · Claude Code · Context Engineering

07-26

Why Harness Engineering Is So Hard

Based on five months of real-world experience (104 commits), this article dissects the structural difficulties of turning LLM demonstrations into reliable products. Key challenges include: inability to write deterministic tests (same input yields different output each time), silent and graded failures (1% error hidden in 99% correct output), debugging natural language paragraphs instead of code (a single adjective can be a bug), the additive instinct trap (prompt growing from 20 to 200 lines causes contradictions), examples steering harder than rules, unstable model foundation (vendor updates silently shift behavior), slow and expensive feedback loops, and invisible work (outsiders think it's just writing prompts). The author argues that harness engineering (prompts, validators, evals, guardrails) is the true moat, with difficulty stemming from the probabilistic nature of the substrate, which cannot be engineered away but only absorbed. Recommended for LLM app developers, AI engineers, and tech leads.

x.com · 16 min · AI Engineering · Developer Tools · LLM

07-25

The new rules of context engineering for Claude 5 models

Anthropic shares updated best practices for context engineering with Claude 5 models (Opus 5, Fable 5). Key finding: previous system prompts and constraints were over-restrictive; newer models have better judgment, allowing significant simplification. They removed 80% of Claude Code's system prompt with no measurable loss on coding evals. Article contrasts old practices (give rules, give examples, put everything upfront, repeat yourself, memory in CLAUDE.md, simple specs) with new ones (let model use judgment, design interfaces, progressive disclosure, simple tool descriptions, auto-memory, rich references). Recommendations: keep CLAUDE.md lightweight focusing on repo-specific gotchas; use Skills as on-demand lightweight guides; use progressive disclosure to avoid context bloat; prefer code references over descriptions. Includes introduction of `claude doctor` command for automatic simplification. For engineers using Claude Code or building Claude-based agents.

x.com · 10 min · Agent Engineering · Claude Code · Context Engineering

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