38 stories tagged prompt-engineering, newest first.
270 points by westsmith | 88 comments
A personal guide to AI image generation covering history, how diffusion and multimodal models work, costs, prompting, and integrating image APIs into code and agents.
6 points by 33a | 2 comments
GitHub repository with a single prompt that turns a GitHub repo into a Claude Code routine-driven automated software factory.
19 points by lowenbjer | 3 comments
GitHub repository for claude-terse, a Claude Code plugin that makes Claude's replies shorter by cutting filler, slogans, metaphors and recaps.
79 points by Theory42 | 51 comments
Blog post introducing the Telegraph Test benchmark, showing that instructing LLMs to answer in telegraph-style cablese cuts output tokens by 40–49% while preserving information.
192 points by saikatsg | 132 comments
A playbook blog post by Addy Osmani on prompting Claude Opus 5.5, steering long runs, and checking results in Claude apps and Claude Code.
177 points by Michelangelo11 | 191 comments
Anthropic's Claude Platform documentation page giving prompting guidance for Claude Opus 5.5, covering behavioral differences from Opus 5, effort calibration, thinking, and agentic tasks.
17 points by FKJ | 2 comments
A benchmark page from the Earn an Honest Dollar agent marketplace testing whether web extractors invent missing fields, showing that a "do not guess" instruction cut fabrication from 70.7% to 20.2%.
116 points by flxflx | 50 comments
Privatemode AI blog post describing how to prompt GLM-5.3-Flash so its first output token gives a decision in a single forward pass, benchmarked against Jev and Laya.
429 points by mellosouls | 328 comments
Anthropic's Claude blog post on updated context engineering practices for Claude 5 generation models.
559 points by bsuh | 272 comments
A personal blog post arguing that AI agents need explicit programmatic control flow rather than more elaborate prompts.
358 points by samcollins | 126 comments
Blog post describing an "underdrawing" technique for getting accurate text and numbers in AI-generated images using Gemini 3.0 Pro.
337 points by pretext | 186 comments
Simon Willison's blog post comparing Anthropic's published Claude system prompts for Opus 4.6 and Opus 4.7, using a git diff to highlight the changes.
809 points by tosh | 344 comments
GitHub repository for Caveman, a Claude Code skill/plugin that cuts output tokens about 75% by making the model respond in terse caveman-style speech.
439 points by killme2008 | 155 comments
GitHub repository offering a drop-in universal CLAUDE.md file that claims to cut Claude output tokens by 63% without code changes.
679 points by objcts | 263 comments
HumanLayer blog post by Kyle Mistele giving guidance on writing an effective CLAUDE.md (or AGENTS.md) file to give coding agents context about a codebase.
828 points by minimaxir | 214 comments
Max Woolf blog post exploring how Google's Nano Banana image model can be prompt engineered for highly nuanced AI image generation.
351 points by cjbarber | 107 comments
GitHub repository for Anthropic's interactive prompt engineering tutorial, with versions for the Anthropic API and Amazon Bedrock.
415 points by samuelstros | 276 comments
A MinusX blog post analyzing what makes Anthropic's Claude Code agent effective, including its system prompts, and how to recreate those design patterns in other agents.
832 points by robotswantdata | 467 comments
Blog post by Philipp Schmid arguing that context engineering, supplying LLMs with the right information and tools, matters more than prompting for AI agents.
400 points by vinhnx | 156 comments
Addy Osmani's Substack article offering a playbook of prompt engineering techniques for getting better results from AI coding assistants.
517 points by miles | 226 comments
GitHub repository for Chain-of-Recursive-Thoughts, an MIT-licensed tool that makes an AI model improve answers by repeatedly arguing with itself.
388 points by dhorthy | 57 comments
GitHub repository by HumanLayer documenting twelve principles for building reliable, production-grade LLM-powered agent applications, modeled on 12 Factor Apps.
409 points by lolptdr | 130 comments
Harper Reed's blog post describing his workflow for building software with LLMs: brainstorm a spec, plan, then execute with LLM codegen in discrete loops.
710 points by rcarmo | 411 comments
Max Woolf's blog post experimenting with whether repeatedly prompting an LLM to "write better code" actually improves the code it generates.
572 points by jascha_eng | 100 comments
Anthropic blog post sharing practical advice and simple composable patterns for developers building effective LLM agents, based on work with customers.
367 points by ViktorasJucikas | 117 comments
A practical guide by Eugene Yan and co-authors sharing lessons from a year of building LLM products, covering prompting, RAG, evaluation, operations, and strategy.
506 points by dinkleberg | 245 comments
An Ask HN thread where users share the custom instruction prompts they use to tailor ChatGPT's responses, starting with the poster's own brevity-focused setup.
329 points by sotasota | 263 comments
A post by John Carmack on X arguing that AI behavior guardrails set via prompt engineering and filtering should be made public.
575 points by Anon84 | 86 comments
Microsoft's free 12-lesson course site teaching beginners how to build generative AI applications, covering LLMs, prompt engineering, and app design.
455 points by simonw | 196 comments
Simon Willison's blog post exploring OpenAI's custom GPTs feature, how to configure them, and examples of GPTs he built.
738 points by spdustin | 366 comments
A GitHub markdown file in the ChatGPT-AutoExpert repo documenting the hidden system prompts ChatGPT uses with GPT-4, including DALL·E and other modes.
371 points by Grimburger | 212 comments
A GenAI Stack Exchange question asking how to stop ChatGPT from excessively apologizing when challenged, even when its reply was correct.
540 points by yla92 | 165 comments
OpenAI platform documentation guide outlining six strategies and tactics for getting better results from GPT models through prompting.
507 points by evanmays | 173 comments
GitHub repository for Microsoft Guidance, a templating language for controlling and structuring large language model outputs.
467 points by appwiz | 88 comments
GitHub repository from Brex containing a guide with tips and tricks for working with large language models like OpenAI's GPT-4.
324 points by Anon84 | 190 comments
Mitchell Hashimoto's blog post distinguishing rigorous prompt engineering from 'blind prompting', a crude trial-and-error approach with little testing.
504 points by mpweiher | 244 comments
Martin Fowler's article recounting how Xu Hao uses chain-of-thought and general knowledge prompting with ChatGPT to produce self-tested code.
387 points by szopa | 156 comments
A Medium post by Ryszard Szopa describing how he taught ChatGPT to speak and translate a constructed language invented by his son.