# prompting Published articles for prompting. This is one page of public article previews, not the complete archive. Follow Next page to continue. Summaries are not the original full articles. ## AI Coding Tip 031 - Stop Over-Prompting Reasoning Models DevFeed: [AI Coding Tip 031 - Stop Over-Prompting Reasoning Models]() Original publisher: [Read original article]() Author: Maxi Contieri Published: 2026-08-08T23:16:29Z Content type: tutorial Language: en Sources: [Maximiliano Contieri - Software Design]() Topics: [ai-coding](), [Prompt Engineering](), [Artificial Intelligence](), [anthropic]() Tags: [ai](), [ai-coding](), [anthropic](), [models](), [prompting](), [reasoning](), [tokens](), [verification]() ### AI overview This article argues that modern reasoning models generally verify and pace themselves, so developers should remove redundant prompts such as "think step by step," "double-check your work," and blanket ALWAYS/NEVER rules. It recommends configuring explicit effort, scope, length, and autonomy settings, while using decision criteria instead of unnecessary absolute instructions. ### Source excerpt TL;DR: Reasoning models already verify and pace themselves, so drop those prompts and set real effort, scope, length, autonomy. Common Mistake ❌ You still write prompts for a model that evolved and s ## How AI-Assisted Coding Is Changing the Engineering Role DevFeed: [How AI-Assisted Coding Is Changing the Engineering Role]() Original publisher: [Read original article]() Author: Wix Engineering Published: 2026-07-19T10:33:35Z Content type: opinion Language: en Sources: [Wix Engineering]() Topics: [Artificial Intelligence](), [Programming](), [Claude Code]() Tags: [2026](), [ai](), [claude-code](), [programming](), [prompting](), [workflow](), [writing-code]() ### AI overview This opinion article examines how AI-assisted coding is changing software engineering. It argues that writing code is increasingly an iterative dialogue involving prompting, evaluation, and pushback, while acknowledging engineers' uncertainty about the future of their work. ### Source excerpt During 2026, I shipped code that I don't fully understand. It was written in a programming language I'm not even fluent in. A year ago, this would have been a fireable offense. There's no way I would have stood on a stage at a Wix Engineering conference, my employers in the crowd, and said that sentence out loud. Last week, it was just another Tuesday. And here's the part that's been keeping me up at night: I was proud of it. That's a paradox, right? If someone else wrote it, I can't be proud... ## The Prompt Is the New Brief DevFeed: [The Prompt Is the New Brief]() Original publisher: [Read original article]() Author: Tushar Deshmukh Published: 2026-06-30T05:45:08Z Content type: opinion Language: en Sources: [UX Magazine]() Topics: [User experience (UX)](), [Artificial Intelligence](), [Framework]() Tags: [ai](), [framework](), [prompt](), [prompting](), [ux]() ### AI overview The article argues that prompting AI is essentially the same skill as writing a clear, context-rich brief. It explains that designers and UX researchers already have this competence and can apply it when working with AI tools. ### Source excerpt Part 2 of the "UX x AI" series. In the first part of the series, we dismantled the most dangerous myth in the design community right now -- that AI is coming to replace the designer. We replaced it with a more accurate and more useful frame: AI is your new intern. Fast, tireless, well-read, The post The Prompt Is the New Brief appeared first on UX Magazine. ## Loop Engineering: Building Systems That Prompt AI Agents Until Goals Are Met DevFeed: [Loop Engineering: Building Systems That Prompt AI Agents Until Goals Are Met]() Original publisher: [Read original article]() Author: Daniel Moka Published: 2026-06-12T12:30:57Z Content type: tutorial Language: en Sources: [Craft Better Software]() Topics: [Loop Engineering](), [Artificial Intelligence](), [Code]() Tags: [agent](), [agents](), [ai](), [article](), [code](), [engineering](), [how-to](), [prompting](), [workflows]() ### AI overview This tutorial introduces loop engineering, an approach in which developers build systems that repeatedly prompt AI agents until a goal is met. It explains how task-by-task prompting makes the developer a bottleneck and presents best practices for using agent-driven loops in software workflows. ### Source excerpt How to Ship Code While You Sleep ## Loop Engineering for Coding Agents DevFeed: [Loop Engineering for Coding Agents]() Original publisher: [Read original article]() Author: Engin Diri Published: 2026-06-09T00:00:00Z Content type: opinion Language: en Sources: [Pulumi]() Topics: [Loop Engineering](), [Claude Code](), [Artificial Intelligence](), [Tooling]() Tags: [agent](), [ai](), [ai-agents](), [automation](), [claude](), [claude-code](), [perspectives](), [platform-engineering](), [prompting](), [pulumi-neo](), [tooling]() ### AI overview An opinion article argues that work with coding agents is shifting from manually writing successive prompts to designing automated loops that identify work, prompt agents, evaluate results, and select next steps. ### Source excerpt For about two years, the unit of work with a coding agent was the prompt. You wrote a good one, you gave it enough context, you read what came back, and you wrote the next one. The agent was a tool, and you were holding it the entire time, one turn after another. That part is ending. Addy Osmani, a director of AI at Google Cloud, has a name for what replaces it, and I have not stopped thinking about it since: loop engineering. You stop being the person who prompts the agent. You design the loop that prompts it for you. In my phrasing: you stop being the thing that runs, and start designing the thing that runs. The leverage moves up a layer. What I want to do here is take an honest look at the pieces, and at the part nobody automates. The leverage moved up a layer The people building these tools have already made the jump. Peter Steinberger has been posting it as a monthly reminder. Peter Steinberger (@steipete) on X. Boris Cherny, who heads Claude Code at Anthropic, says the same thing about his own job. He does not prompt Claude anymore. He has loops running that prompt Claude and decide what to do next, scanning the issue tracker, the team chat, and the timeline for what to build. "My job is to write loops." A loop is a goal that prompts itself. You set the purpose, and the system keeps iterating until it's met. In practice it finds the work, hands it out, checks the result, writes down what got finished, and decides the next thing, then it pokes the agent instead of you. You build that small system once and let it run. Look closer, and a loop is really two loops nested. The inner one does the work against a spec. The outer one decides what the work should be: it watches an issue tracker, an error feed, a changelog, then writes the next spec and hands it down. Most people are still running that outer loop by hand, in their head, and calling it a backlog. The part that surprised me is that this is barely a tooling problem anymore. A year ago a loop meant a pile of ## Figma Make, now on your local code DevFeed: [Figma Make, now on your local code]() Original publisher: [Read original article]() Author: Iris Lin; Jesse Lumarie Published: 2026-05-28T12:00:00Z Content type: release Language: en Sources: [Figma Blog]() Topics: [Figma](), [Code](), [ui]() Tags: [agent](), [code](), [collaboration](), [design](), [figma](), [prompting]() ### AI overview Figma announces new beta capabilities for Figma Make that let users connect to a production codebase, make visual UI edits, and ship code changes from within Figma. ### Source excerpt From visual editing to contextual prompting and collaboration, Figma Make is expanding how teams can design with code. ## 7 tips for using Figma Make credits more efficiently DevFeed: [7 tips for using Figma Make credits more efficiently]() Original publisher: [Read original article]() Author: Alexia Danton Published: 2026-05-22T12:00:00Z Content type: tutorial Language: en Sources: [Figma Blog]() Topics: [Figma](), [Code](), [ui]() Tags: [best-practices](), [code](), [component](), [context](), [cost](), [figma](), [prompting](), [request](), [screen](), [state](), [structure](), [tips](), [ui]() ### AI overview This tutorial presents seven practices for using Figma Make credits more efficiently. It recommends writing a complete initial prompt, keeping follow-up prompts focused and specific, building in stages, grouping related changes, and switching to direct editing for small visual adjustments. ### Source excerpt While everyone's talking about "tokenmaxxing," we've compiled seven best practices to help you build smarter--without prompting more--in Figma Make. ## Generate editorial React presentations with one Claude Code prompt DevFeed: [Generate editorial React presentations with one Claude Code prompt]() Original publisher: [Read original article]() Author: Ishan Khanna Published: 2026-05-17T08:17:35Z Content type: tutorial Language: en Sources: [Ishan Khanna]() Topics: [Claude Code](), [React](), [Code](), [Large Language Model](), [Tailwind CSS](), [Vite]() Tags: [animation](), [architecture](), [claude-code](), [code](), [how-to](), [keyboard](), [llm](), [llms](), [presentation](), [prompt](), [prompting](), [react](), [tailwind](), [vite]() ### AI overview A tutorial presents a detailed Claude Code prompt for generating five-slide React presentations as a single Presentation.jsx file. The prompt fixes the design system and narrative structure while allowing the topic content to change, and the result can be used in a Vite, React, and Tailwind project. ### Source excerpt If you caught my YouTube Short on generating presentations as React apps with an LLM, this is the prompt I promised. Paste it into Claude Code, edit the CONTENT block at the top, and you get back a single-file Presentation.jsx -- five slides, keyboard nav, animated counters, an ## The End of Prompting: Why the Future of AI Experience Design Is Constraint-First DevFeed: [The End of Prompting: Why the Future of AI Experience Design Is Constraint-First]() Original publisher: [Read original article]() Author: Yves Binda Published: 2026-04-17T04:57:58Z Content type: opinion Language: en Sources: [UX Magazine]() Topics: [User experience (UX)](), [Artificial Intelligence](), [Prompt Engineering](), [Language models]() Tags: [ai](), [experience-design](), [interface](), [prompting](), [systems](), [ux]() ### AI overview This commentary argues that prompting should not be treated as the primary interface or design philosophy for AI systems. It presents constraint-first design--embedding verification, scope boundaries, and escalation paths into system architecture--as a more reliable approach for high-stakes and regulated workflows, because authoritative-sounding language does not guarantee accuracy or compliance. ### Source excerpt There is a point in each technology cycle when the workaround turns into the product. We saw it when spreadsheets displaced paper ledgers and then became the planning tool. Emails supplanted memos, which then became the management layer. And now we're watching that happen with prompting. Prompting was never meant to be the interface. It The post The End of Prompting: Why the Future of AI Experience Design Is Constraint-First appeared first on UX Magazine. ## AI-generated ARM assembly optimization reduces instruction count eightfold in a C++ string-counting test DevFeed: [AI-generated ARM assembly optimization reduces instruction count eightfold in a C++ string-counting test]() Original publisher: [Read original article]() Author: Daniel Lemire Published: 2026-04-05T21:16:14Z Content type: opinion Language: en Sources: [Daniel Lemire]() Topics: [Assembly](), [Optimization](), [Artificial Intelligence](), [C++](), [Compiler]() Tags: [ai](), [assembly](), [c-plus-plus](), [claude](), [compiler](), [optimization](), [performance](), [prompting]() ### AI overview The article describes an experiment using Grok and Claude to repeatedly optimize an ARM assembly function that counts a character across strings. On random strings of up to 1 kilobyte, the optimized versions reduced instructions by a factor of eight and achieved similar running-time reductions, though the author notes that the code was not closely examined for mistakes. The best version could be rewritten in C using SIMD intrinsics, so assembly provided no benefit in this case. ### Source excerpt Suppose you have several strings and you want to count the number of instances of the character ! in your strings. In C++, you might solve the problem as follows if you are an old-school programmer. size_t c = 0; for (const auto &str : strings) { c += std::count(str.begin(), str.end(), '!'); } You can ... Continue reading Can your AI rewrite your code in assembly? ## Issue no.15: The state of design DevFeed: [Issue no.15: The state of design]() Original publisher: [Read original article]() Author: Figma Published: 2026-03-16T21:35:00Z Content type: article Language: en Sources: [Figma Blog]() Topics: [Artificial Intelligence](), [User experience (UX)](), [Figma](), [Canvas](), [Claude](), [Claude Code](), [Model Context Protocol]() Tags: [ai](), [claude](), [claude-code](), [figma](), [mcp](), [product-development](), [prompting](), [quality](), [research](), [workflows]() ### AI overview Figma's State of the Designer research examines how AI is affecting design work, hiring, and the skills designers need. The article reports that 91% of respondents say AI tools help improve their work, while 82% of hiring managers say demand for designers has stayed stable or increased. ### Source excerpt AI-powered tools and workflows are fundamentally changing the way we design. ## How to write a good spec for AI agents DevFeed: [How to write a good spec for AI agents]() Original publisher: [Read original article]() Author: Addy Osmani Published: 2026-01-19T15:31:16Z Content type: tutorial Language: en Sources: [Elevate]() Topics: [AI-assisted coding](), [AI Agent](), [Code](), [context window](), [Claude Code]() Tags: [agents](), [claude-code](), [cli](), [coding](), [coding-agents](), [context-window](), [how-to](), [prompting]() ### AI overview A practical guide to writing specifications for AI coding agents. It recommends starting with a concise high-level vision, adding enough structure, style, testing, and boundaries to guide the agent, breaking large tasks into smaller ones, and iterating on a plan before implementation. ### Source excerpt How to structure, plan, and iterate for high-performance coding agents ## Improve AI output with continuous improvement DevFeed: [Improve AI output with continuous improvement]() Original publisher: [Read original article]() Author: Alexander Nohe; Dan Gruhl Published: 2026-01-13T00:00:00Z Content type: tutorial Language: en Sources: [Firebase Blog]() Topics: [Artificial Intelligence](), [Chain-of-thought]() Tags: [adk](), [agentic](), [ai](), [automated](), [firebase](), [gemini](), [prompting](), [python](), [rewrite](), [verify]() ### AI overview A tutorial explains continuous improvement, a prompting strategy in which one AI generates an output and another AI critiques it before the first AI revises the result. It demonstrates the approach with ADK and LoopAgent for designing gym workouts. ### Source excerpt Learn how to improve your AI outputs to a more reliable output through continuous improvement. ## Cooking with constraints: A designer's framework for better AI prompts DevFeed: [Cooking with constraints: A designer's framework for better AI prompts]() Original publisher: [Read original article]() Author: Greg Huntoon Published: 2026-01-05T06:00:00Z Content type: tutorial Language: en Sources: [Figma Blog]() Topics: [Prompt Engineering](), [LLM Techniques](), [Language models](), [Framework]() Tags: [ai](), [design](), [designer](), [framework](), [language-models](), [llms](), [prompt](), [prompting]() ### AI overview This article presents a framework for writing better AI prompts in design work. It argues that clear instructions, context, and constraints help reduce ambiguity and make probabilistic model outputs more structured, repeatable, and intentional. ### Source excerpt Design and cooking share a truth: Preparation determines the outcome. Structured prompts turn AI from guesswork into a reliable design partner. ## Firebase Studio prompting tips for building web applications DevFeed: [Firebase Studio prompting tips for building web applications]() Original publisher: [Read original article]() Author: Tianzi Cai Published: 2025-07-07T00:00:00Z Content type: tutorial Language: en Sources: [Firebase Blog]() Topics: [Firebase](), [Prompt Engineering](), [web applications](), [App](), [dashboards]() Tags: [ai](), [app](), [applications](), [blog-post](), [build](), [developer-experience](), [firebase](), [firebase-studio](), [gemini](), [next-js](), [prompt](), [prompting](), [prototyping](), [tips](), [tips-and-tricks](), [tutorials](), [typescript](), [web-applications]() ### AI overview This tutorial explains how to write effective prompts for Firebase Studio's App Prototyping agent. It recommends concise elevator-pitch-style initial prompts that describe an application's purpose, behavior, and appearance, while noting that the Prototyper is designed to generate no more than 10 core features on the first try. ### Source excerpt News, tutorials, and updates from the Firebase team. ## TDD: The Missing Protocol for Effective AI Assisted Software Development DevFeed: [TDD: The Missing Protocol for Effective AI Assisted Software Development]() Original publisher: [Read original article]() Author: John Riccardi Published: 2025-05-28T18:14:00Z Content type: tutorial Language: en Sources: [8th Light Insights]() Topics: [Test-driven development](), [Large Language Model](), [AI Development](), [Software Engineering](), [Prompt Engineering]() Tags: [ai](), [coding](), [core](), [engineering-and-devops](), [llm](), [prompting](), [quality](), [software-development](), [tdd]() ### AI overview This article argues that test-driven development provides a structured communication protocol for collaborating with AI coding tools. It explains that large language models often struggle with broad, ambiguous software tasks because developers provide insufficient context, constraints, and edge cases, and recommends writing tests first before using AI to implement solutions. ### Source excerpt Bottom Line Up Front The goal isn't to replace human developers but to offload repetitive tasks so we can focus on creativity and architecture--where human expertise is irreplaceable. Start your next feature by writing tests first, then let AI help implement the solution. You'll deliver higher quality code faster, with greater confidence. Large Language Models Aren't as Simple as They Seem Large language models (LLMs) have a fundamental flaw: they appear deceptively easy to use. That blinking cursor invites you to interact with an LLM as if it were another human being--one that understands your intent, the context of your questions, and the logic behind your requests. You type a prompt, hit send, and then disappointment sets in. While it's exciting to watch it generate multiple files of code for your new project, the moment you try to run that code, it often fails to work as intended or doesn't run at all. As you continue prompting it to iterate on previous outputs, the LLM may go in circles, change direction entirely, or simply stall. The core issue is that we ask LLMs to do too much while providing too little direction and context--effectively setting them up for failure. It's like the classic PB&J experiment, where a father follows his children's sandwich-making instructions literally. We believe we're being clear, but we drastically underestimate how much implicit context AI lacks about the problems we're trying to solve. What we need is a better communication protocol--one that structures our requests in a way AI tools can reliably understand and execute. Why AI Struggles With Large, Ambiguous Problems Despite their impressive capabilities, current LLMs consistently struggle with large, vague problems. The issue is not primarily token limitations or technical constraints; it's a deeper problem in how we frame our requests. When developers ask AI to "build a complete authentication system" or "create an e-commerce checkout flow," they inadvertently set the AI up to ## Vibe Coding netlab Lab Topology with ChatGPT DevFeed: [Vibe Coding netlab Lab Topology with ChatGPT]() Original publisher: [Read original article]() Published: 2025-05-19T05:08:00Z Content type: article Language: en Sources: [ipSpace.net blog]() Topics: [coding](), [ChatGPT](), [Vibe coding]() Tags: [ai](), [chatgpt](), [coding](), [netlab](), [prompting](), [vibe-coding]() ### AI overview The author tests whether ChatGPT can generate a netlab topology from a prompt. ChatGPT initially produces a topology with incorrect indentation, then corrects it after the author provides the resulting error message. ### Source excerpt I was considering an AI add-on that would have access to the netlab documentation and help you figure out how to use it for a few years, but never got around to implementing it (and surprisingly, with all the AI hype out there, there were no volunteers submitting pull requests). A few weeks ago, someone suggested adding an MCP server as an interface to ipSpace.net content, but the discussion quickly devolved into vague ideas. However, as ChatGPT now has access to the live Internet, I decided to try out whether it can get the job done with a bit of prompting. TL&DR: After a hiccup, it worked surprisingly well. Read more ... ## Treat AI Like a Senior Dev, Not a Junior One DevFeed: [Treat AI Like a Senior Dev, Not a Junior One]() Original publisher: [Read original article]() Author: Andrew Tate Published: 2025-04-24T16:51:24Z Content type: article Language: en Sources: [Blog -- Neon Docs]() Topics: [ai-coding](), [Prompt Engineering](), [Artificial Intelligence](), [Large Language Model](), [Vibe coding](), [Testing]() Tags: [ai-coding](), [hallucinations](), [product](), [prompting](), [quality](), [testing](), [vibe-coding]() ### AI overview This article argues that developers should treat AI as a senior development partner rather than a junior coder. It recommends rigorous prompting with context, examples, constraints, and expected outcomes to produce more reliable software and reduce hallucinations, slop, missed edge cases, and rework. ### Source excerpt Most developers approach AI coding with the wrong mental model. The tendency, exacerbated by the vibe coding trend, is to let AI take the wheel. "Build me a UI using Tailwind that lets users see their account info" is an example initial request, followed by more and more short (a... ## 45 Minutes, 100% AI-Generated Code DevFeed: [45 Minutes, 100% AI-Generated Code]() Original publisher: [Read original article]() Published: 2025-04-19T00:00:00Z Content type: article Language: en Sources: [Mutt0-ds Notes]() Topics: [Vibe coding](), [AI tools for developers](), [Artificial Intelligence](), [AI Agent](), [cursor](), [dashboards](), [Next.js](), [web applications](), [Figma]() Tags: [ai](), [ai-agent](), [ai-tools-for-developers](), [coding](), [cursor](), [figma](), [next-js](), [prompting]() ### AI overview The author describes building an interactive StoryTeller dashboard with an AI agent in 45 minutes using entirely AI-generated code. The article argues that AI tools accelerate research and prototyping but still require critical thinking, while blindly accepting generated code can create technical debt. ### Source excerpt Last Saturday, I woke up with an idea: what if StoryTeller, the product I work on, had an interactive dashboard with an AI agent that could... ## Solving the Prompt Management Problem DevFeed: [Solving the Prompt Management Problem]() Original publisher: [Read original article]() Author: Emmanuel Goossaert Published: 2025-04-09T11:52:00Z Content type: article Language: en Sources: [Code Capsule]() Topics: [prompt](), [Web](), [JSON](), [ChatGPT](), [Prompt Engineering]() Tags: [agentic-ai](), [ai](), [ai-ai-ai-models-artificial-intelligence-large-language-models-llms-productivity-prompt-engine](), [ai-models](), [artificial-intelligence](), [chatgpt](), [code](), [json](), [large-language-models](), [llms](), [productivity](), [prompt](), [prompt-engineering](), [prompt-library](), [prompting](), [templates](), [web]() ### AI overview The author introduces Prompt Library, a web-based tool for creating, editing, searching, tagging, and composing prompts. It stores data locally, supports JSON export, and may later add prompt history, version comparison, and prompt paths. ### Source excerpt I developed Prompt Library, a web tool for organizing prompts with features like editing, tagging, and exporting. The post Solving the Prompt Management Problem first appeared on Code Capsule. ## Designing software with AI DevFeed: [Designing software with AI]() Original publisher: [Read original article]() Author: Mike Published: 2023-06-14T03:36:00Z Content type: tutorial Language: en Sources: [Dev Details]() Topics: [Artificial Intelligence](), [Software](), [coding](), [App](), [prompt](), [prompting](), [service](), [ChatGPT](), [Code Challenge](), [client]() Tags: [ai](), [analytics](), [application](), [chatgpt](), [coding](), [guide](), [mapping](), [practical](), [prompt](), [prompting](), [real-world](), [service](), [software]() ### AI overview Part 2 of a practical series showing how to use AI as a collaborator when designing software. The article uses a URL shortener as a practical example and demonstrates how ChatGPT 3.5 guides problem-context and use-case discussions, with ChatGPT 4 producing better results in a test run. ### Source excerpt Part 2 of a practical example of using AI for coding