AI Users Struggle with Prompt Management

Different answers for same prompt?

“Does anyone have a playbook they are willing to share, or an AI prompt to build one?”

“So I have tried all the tasks plugins I can find so far none have really aligned with the system i want to use. The closest is using the tasks plug in as it in inline, I like that it isn't a new note on its own so it just lives on whatever note i have it in. This is good to me because for projects, I can just add the tasks easily onto that projects note page and not have to rewrite it as a task or a reminder todo checklist on the note. I have also managed to link the task plug in to daily no”

“Free and open source (MIT license). Repo: https://github.com/lanveric/clarify-crit **What it is:** a skill called Clarify (CRIT) that sits in front of a request and decides, before your AI acts, whether it actually understands what you're asking for. If it does, it gets out of the way. If there's real ambiguity, it asks the smallest number of questions that resolves it, not a generic intake form. Design principle it's built around: > Use the least interaction and least visible str”

“🔴 I’ve come to think that everyone develops their own prompting style over time. There probably isn’t a single “best” method it depends on what you’re trying to do or the kind of result you want and how much direction the model needs. For a long time I leaned heavily on persona based prompts. I’d spell out the role I wanted the AI to take on and then add details like its area of expertise, point of view, tone, communication style, and goals. That approach has worked well for me especially when”

“I have been building more repeatable AI workflows lately, and the prompt text is usually the smallest part of what makes them dependable. The useful package tends to include the job the prompt is supposed to do, the inputs it expects, a clear output contract, one good example, a short review checklist, and the failure cases that should stop the workflow. A clever paragraph can work once. A small prompt SOP is much easier to reuse, test, and hand to someone else. When you save a prompt that you e”

“Im currently doing some AI courses and im seeing a large number mention using XML tags in large prompts to lower the chance of models misunderstanding different part of the prompt. Is this something people are actively using? have you really noticed much difference in models effectively understanding prompts? submitted by /u/Livid_Salary_9672 [link] [comments]”

“We developers copy and paste code, logs to the AI assistants and browsers. Hidden inside that code can be API keys, access tokens, passwords, certificates, or other secrets that were never meant to be shared. if i develop a chrome extension to scans the content you’re about to paste directly within your browser and alerts you whenever sensitive information is detected. You can cancel the paste, automatically redact the detected secrets or continue if you’ve reviewed the content. Will the users a”

“Hi everyone, For a long time, my prompt engineering workflow was a complete mess. I kept my prompts in local markdown files, had to manually replace variables like {{target_audience}} or {{tone}} every single time, and constantly copied and pasted them back and forth between Claude, ChatGPT, and Gemini. Even worse, whenever I tweaked a prompt, I often broke it and couldn't remember what the previous, working version looked like. To solve this for myself, I spent the last few months building”

“I’ve started turning complex, repetitive prompts into reusable AI skills instead of constantly expanding one giant prompt. My basic workflow: Identify where a normal prompt produces inconsistent results Break the task into clear steps, rules and output formats Add checks for common mistakes and edge cases Test it with different inputs and refine the instructions Include practical outputs instead of only recommendations I recently published a bunch of free skills, here are two of them: Color Matc”

“I'm looking for people to try this out, give feedback, and help improve it. It's a ~17k word meta-prompt for Claude Projects that interviews first, routes to the right kind of artifact second, and builds last. What it does: Building Turns your ideas into finished solutions through a gated interview: 2-3 questions per round, readiness indicators (🔴🟡🟢), and a coverage ledger it must fill before it's allowed to generate. Say "Go" at any point to skip and it flags its assump”

“Most prompt work is iterative: you nudge, re-roll, chain. I wanted the opposite constraint: can you compress your whole vision into a single prompt that a model executes once? So: one prompt → one model → one shot. That output is your entry. No do-overs. The prompt runs server-side (your key, encrypted, used once, deleted) and we hash prompt + output so it's a provable single shot. Prompts stay sealed until the end, then get revealed, so the interesting part afterward is reading how people s”

“I have a 'ticket' out now with Support. But I am working around it as well. I branched off the thread and just tweak my prompts better for scenes. Once or if they fix the writing block issue, I return to the original threads to continue working on 'scenes' as I call them.”

“Hi everyone, I was hoping the good lads here would share what you guys use to extract all the information from a project. I'm looking for a prompt which could gather and collate all the information about the project discussed in the project. PS: I hope to pin and update the post with the most effective prompts. Thanks! submitted by /u/Recalcitrant_mango [link] [comments]”

“built parser to automate alerts last year. spent weeks on sliders, ended up replacing them with simple yes/no queue because output kept breaking.”

“I did, and since you said it was a command line, I tried to use regular command line commands. I was told that they were not valid commands.”

I built a prompt library specifically for image prompts because I kept losing prompts I'd already refined. Now I can quickly save, organize, and reuse them instead of rewriting them from scratch.…

“What is the best way to create a useful internal prompt library for a team? submitted by /u/Spare_Flamingo7879 [link] [comments]”

“In our product we have a lot of prompts that we use in production, from data extraction, to template generation, spell checking, assisted writing, you name it. We tried several tools, from the early days of gpt3 where no structured output was available (we used typechat to force structured output through ts interfaces) to other tools now (i.e Portkey which offers prompt management). There is several things I don't like about the workflow and that really don't feel well thought, but if I”

“I've been creating more content with AI lately and keep coming across people talking about normal prompts, JSON prompts, and action prompts. I mostly use regular prompts, but I'm curious how others approach this. For those of you creating blog posts, social media content, marketing copy, videos, or other AI-generated content: When do you stick with a normal prompt? When do you switch to a JSON prompt? What exactly is an action prompt, and when does it make more sense? Have you noticed be”

“Over time, I noticed that many of my prompts for lead research and business-data workflows followed the same recurring patterns: Evaluate companies against an ICP Normalize inconsistent job titles Research a company without inventing missing information Identify gaps before drawing conclusions Review possible duplicate records conservatively Segment companies and people using explicit criteria Instead of keeping these as isolated prompts, I converted them into 24 reusable Agent Skills: https://g”

“We recently used a feature in a screen recording tool that changes how we work with AI. Instead of writing long prompts explaining UI bugs or feature requests, you simply: • Record your screen • Point out the issue • Upload the recording to Claude Claude understands the visual context, so there's much less prompt writing involved. We're looking for honest feedback from developers. Would this fit into your workflow? submitted by /u/NumberHorror2756 [link] [comments]”

“This is the reference image I am trying to practice to write prompt for: https://fiverr-res.cloudinary.com/image/upload/f_auto,q_auto/v1/attachments/project_item/attachment/7405ae7bb33b89a7e519bb8c2136e3dd-1762428267372/2.png And this is what I could generate with my prompting skill: https://ibb.co/CpSfwTWZ prompt: a close up image of a young confident women in her 20s with short shoulder length straight dark hair, brown coloured eyes, natural skin, visible pores and some freckles, slightly rota”

“This is the reference image I am trying to practice to write prompt for: https://fiverr-res.cloudinary.com/image/upload/f_auto,q_auto/v1/attachments/project_item/attachment/7405ae7bb33b89a7e519bb8c2136e3dd-1762428267372/2.png And this is what I could generate with my prompting skill: prompt: a close up image of a young confident women in her 20s with short shoulder length straight dark hair, brown coloured eyes, natural skin, visible pores and some freckles, slightly rotated away from camera, ho”

“Your prompt library is useless if you can't access it when you're actually using AI I've collected hundreds of prompts over time. Some are in Notion. Some are in Google Docs. Some are in random text files. The problem was never saving prompts. The problem was: "How do I find the right prompt when I'm already inside ChatGPT?" Usually the workflow looked like: Need a prompt ↓ Leave ChatGPT ↓ Search Notion ↓ Copy prompt ↓ Return to AI chat ↓ Paste The AI response itself on”

“No hate, but it seems like you're charging $29 for a prompt? What's this do that coping and pasting into my chat window and saying "rewrite to sound more ..." doesn't do?”

“A couple weeks ago I shared PromptChain here a small Streamlit app that chains two models: a little Prompter that rewrites your rough idea into a proper prompt, then a larger Coder that turns that prompt into code. The whole point is that on an 8–16 GB card you can usually only hold one model at a time, so it auto-unloads one before loading the other no manual swapping, no copy-pasting between two chat windows. The comments last time turned into a real to-do list, so here's what's landed”

“I want a favor from the community, more like tell me how to improve this tool . In short this is a chrome extension which sits beside your ai chatbot input field. It does 3 things 1. Prompt : Make your current input into a better prompt 2. Grammar : Corrects your grammar mistakes you made 3. Short : Make your current input into a short text without losing its meaning What my goal to achieve is I want to improve this tool to increase their small time in productivity because the reason for creatin”

“Like a lot of people, I bounced between Midjourney, SD and Flux — and kept being frustrated that a prompt that worked great in one model produced garbage in another. So I went down the rabbit hole of why . Here's what I found: Stable Diffusion (SD1.5/SDXL) responds best to weighted, comma-separated tags: portrait of a female alchemist, dramatic studio lighting, (renaissance oil painting:1.3), intricate golden jewelry, bokeh background . Token order matters — CLIP weighs early tokens more hea”

“For prompt engineers: comparing the same prompt across many models is painful (different keys, dashboards, limits). Sharing a free, MIT, self-hosted tool that fixes it (disclosure: I'm the maintainer). One endpoint, 237 models (90+ free). Switch models by name and run one prompt across GPT-, Claude-, Gemini-, DeepSeek-class models to compare — without juggling accounts. 90+ have free tiers, so a lot of prompt testing costs nothing. Output Styles. Apply named styles at the gateway (terse-pros”

“Kept it deliberately small: no bloated dashboard, just search a goal → get an ordered set of steps → each step has a copy-paste prompt, a checklist to verify it worked, and a recovery prompt for when it didn't. There are ~100 flows across app-building, debugging, launch, QA, and agent topics, plus custom generation when nothing matches. https://flows-ai.emergent.host/ For the micro-SaaS folks: at what point did you add auth/paywall vs. keeping it fully open? Trying to decide the right moment”

“A library gives you 500 prompts and zero guidance on sequence. For multi-step builds that falls apart fast. What's worked better for me is structuring prompts as workflows: Context pack first — a reusable block that teaches the model the project constraints before any task. Ordered steps, each with one focused prompt (kept in basic / detailed / aggressive variants). An explicit "expected result" so you know what good looks like. A verification checklist per step before continuing.”

“With the Claude fable 5 release I have seen many articles on people making full games with just a few prompts like universe and even Hogwarts castle. Chatgtp 5.6 benchmark shows it with a significantly higher score than fable 5 for 5.6 sol. Will it be just as good at coding? No one knows for sure but just curious on others thoughts. For reference I have both chatgtp and Claude subscription and am constantly switching between them . Chatgtp is generally good at writing and somewhat coding but won”

“Many prompt tools focus on rewriting prompts. This prototype takes a different approach. It compiles your intent through a structured governance pass before execution by identifying likely constraints, surfacing ambiguity, and producing an explicit specification before execution, and showing the transformation steps and diagnostics used during compilation. It makes its transformation process transparent. It's called Re-Prompt. This is a working proof of concept, not a finished product, and I”

“If you want to evaluate prompt outputs without reading every single one, there are basically three grader types: 1. Deterministic graders. Exact match, regex, JSON schema checks, small scripts. Best for: structured output, extraction, classification. Fails when: quality is subjective. You can't regex "is this summary good". 2. LLM-as-judge. A model grades the output against criteria you define. Best for: tone, helpfulness, correctness of free text. Fails when: you're not explic”

“After months of iteration, I decided to open source my personal prompt collection. It includes three production-focused prompts: VECNA — Performance and efficiency auditor with quantified optimization recommendations. BLOB — Security and production code auditor covering security, logic, concurrency, performance, and integration. ZETA — Prompt optimizer that transforms vague requests into structured prompts for different LLMs. Each prompt is available in native formats for Claude, GPT, Gemini, Ll”

“Automated prompt-opt is basically search over prompts. You give it a starting prompt and a scoring function, it generates variants, scores each one, keeps the best, and repeats. The popular tools differ mostly in how they pick the next variant: ProTeGi looks at what failed and writes a text "gradient" describing what to change, GEPA runs an evolutionary loop and recombines the prompts that survived, Bayesian search builds a model of the score surface and samples where it predicts a gai”

“One thing I keep running into is that a prompt which reliably returns clean structured output on GPT4 will completely fall apart on Claude or Mistral. The formatting goes sideways, the JSON breaks, or the model just decides to add extra commentary I never asked for. I've been experimenting with a few approaches. Explicitly stating the output schema in the system prompt helps, but only to a point. Adding a worked example in the prompt itself gets me closer to consistency. Some people swear by”

“Is anyone actively doing this? This is not a novel concept. We hate waiting for prompt processing, and I envision a solution that lets us save prompts as prompt templates. Ideally, it would be a plugin or middle layer between llama cpp or vllm. The workflow is simple, identify prompts you are constantly running and snapshot them so that you can easily reuse them. Then when you launch a new chat you have the option to load a template. Build a collection of knowledge that you always find useful an”

“I use ChatGPT, Claude, and Gemini every day. One thing kept slowing me down wasn't writing prompts—it was moving information between prompts. A typical workflow looked like this: Research → Outline → Draft → Review or Code Review → Refactor → Generate Tests Every single time I had to: Copy a prompt from Notion Paste it into ChatGPT Wait for the response Copy the AI output Paste it into the next prompt Repeat... It didn't feel like I was working with AI—it felt like I was acting as the co”

“The way most teams manage prompts: a Google Doc. Or a comment in the codebase. Or worse — hardcoded strings scattered across multiple files with no history of who changed what and why. Then a prompt change breaks production. You have no audit trail. You don't know what the previous version was. You can't roll back. You start debugging by memory. The system treat prompts like code. Here's the full governance model. Slugs (not UUIDs) for human-readable delivery: Every template gets a U”

“I want to turn my app into a prompt hub for my AI prompts. I already have a couple of templates that help me include the important context I would otherwise forget. I’m curious how others organise their setup. For example: Do you group prompts by task, client, tool, or topic? Do you use naming conventions for abbreviations? And how do you name the other snippets? Do you use placeholders or variables? Do you store full prompts or smaller reusable prompt blocks? How do you keep the whole thing fro”

“so i have been working to create an prompt generator for myself. i asked claude and chatgpt on how would i do it. both of them pointed me to creating a master prompt which combined with user prompt and fed to LLM's Api will return back enhanced prompt which is intended result user gives prompt > (master prompt + user prompt) > sent to LLM api LLM api > enhanced prompt > user gets enhanced prompt i just want to know from community that is there better way to do this. if anybody kn”

“After enough prompting I noticed weak prompts mostly fail for the same handful of reasons: no role or quality bar, no examples to anchor the format, no self-check, sloppy output structure, or no decomposition on a multi-part task. So instead of task-specific prompts, I keep 5 scaffolds that encode those patterns, each with {{variables}} I fill in. They work on almost anything. Copy them, swap the {{variables}} , and reuse. 1. Role + Rubric - forces consistent quality instead of vibes You are {{s”

“I have a project folder with a prompt I use to learn stuff. The problem is it keeps trying to explain things in terms of previous problems I’ve asked about. I have memory disabled, how do I actually turn it off? submitted by /u/ParkingFabulous4267 [link] [comments]”

“Prompt iteration is usually vibes — you tweak wording, eyeball the output, and forget what you tried. I wanted a structured scoreboard for it. Prompt Tournament Runner: enter 3–5 variants for one task, paste the outputs, score each 1–5, pick a winner. Every run saves locally to a JSON file. No SaaS, no telemetry, no API keys — runs entirely on your machine. Feedback welcome, especially on the scoring flow: github.com/oleg-koval/prompt-tournament-runner submitted by /u/noir_cafe [link”

“Lately, my workflow has basically just been bouncing prompts back and forth with AI, changing 5 different files across HTML, CSS, and JS in a couple of minutes. The problem is, it’s incredibly easy to lose track of what actually changed, and then everything crashes because of a stupid typo or an unclosed block that the AI introduced. ​I just deployed PatchTrail to the marketplace. It’s designed to solve a major pain point when building apps or "vibe coding" with AI: losing track of cha”

“This started as a personal annoyance. Every AI coding assistant I use wants its own context file - Claude Code reads CLAUDE.md, Cursor wants .cursor/rules, then there's AGENTS.md, GEMINI.md, Copilot instructions. Same project info, rewritten in a slightly different format each time. I got tired of it. So I spent a weekend building claude-init. You run it in a repo, it reads what's actually there - the stack, the npm scripts, the folder layout, env vars, a bit of git history - and writes”

“Spent months debugging what I thought was a "bad prompt" problem. Turned out to be a token allocation problem wearing a prompt mask. Short version of what I found: When your prompt shares token budget with a large context window, the model starts deprioritizing your instructions. Not ignoring them. Deprioritizing. The behavior looks like inconsistency. It reads like the model "forgot" what you told it. It is actually just arithmetic. The fix I landed on was separating instruc”

“I’m trying to find a better workflow for batch text to image generation. My goal is to generate 10–40 separate images from individual prompts without manually copying/pasting and clicking generate every time. I’m creating historical documentary style images where accuracy matters (uniforms, props, hands, equipment, period details). The issue is, when I use ChatGPT image generation manually (DALL-E / GPT image), my usable success rate is around 85–90%. I tried building a custom HTML batch tool wi”

“Transforme un Prompt simple en un Prompt alineado a la estructura de Fable 5. De un Prompt original con más de 300 palabras, Fable 5 lo convierte en un Prompt más riguroso con menos de 165 palabras. submitted by /u/Great-Yak-7602 [link] [comments]”

“For artifact creation. Fill in the templet then feed to the LLM of your choice. # ARTIFACT CREATION TEMPLATE ## Mission [State exactly what artifact to create and what it must accomplish.] ## Mission Win Criteria - [ ] [Criterion 1] - [ ] [Criterion 2] - [ ] [Criterion 3] - [ ] Output is in Markdown inside a single code block - [ ] Output includes a verification section - [ ] No roleplay or "you are" language is used ## Required Inputs Do not generat”

“Had a Claude project that kept giving me confident, slightly wrong output for a week. So I did what every thread on here tells you to do. Rewrote the prompt 14 times. Added XML tags, a role, examples, a 9-step instruction chain. Output got 10% better. Then plateaued. What finally moved it: loading the brand voice doc, last week's approved post, and the ICP file into the model's context before it ever saw my prompt. The actual prompt at the end was 4 lines. Honest take: prompt engineering”

“At some point plain prompts stop scaling. You add conditions, exceptions, edge cases and suddenly the prompt is a wall of text that the model half-ignores and you can barely maintain. I started looking into Rulemapping, a methodology originally developed to make legal texts machine-readable. The core idea is to break down complex rule systems into explicit conditions, outcomes, and exceptions in a format that both humans and machines can process without ambiguity. If it works for legislation, it”

“Hey r/PromptEngineering , I want to share an experiment that's really about prompt design as much as code. The context: GymCoach is an open-source, self-hosted hypertrophy training tracker with a built-in AI coach (Next.js 14 + TypeScript, Prisma/Postgres, Docker). The coach builds a compact, structured payload from your profile, recent sessions, active program and per-exercise progression — then suggests program changes that are Zod-validated before anything touches your data. Provider-agno”

“I spent weeks compiling every AI prompting framework worth knowing. Here's the full cheat sheet. Most threads talk about one or two frameworks. Nobody puts it all in one place. So I did. 8 techniques. 7 key terms. 10 frameworks. 12 best practices. If you're getting lazy outputs from AI, the problem is your prompt structure, not the model. TREF works for most tasks. GRADE if you're doing marketing. PECRA for anything complex. submitted by /u/writebysachin [link] [com”

“https://pub.towardsai.net/prompt-chaining-build-a-linked-sequence-that-delivers-the-whole-project-prompt-to-profit-day-14-4a85d347d213 submitted by /u/IntelligentSam5 [link] [comments]”