AI Prompts Get Rewritten From Scratch Every Time
AI users across various workflows are experiencing significant challenges in managing, organizing, and reusing prompts. This leads to wasted time, inconsistent results, and frustration as prompts become scattered, difficult to iterate on, and prone to being rewritten. The need for better prompt organization and engineering tools is evident.
SOURCES (60)
“Before Submitting [x] I searched open and closed issues and discussions for an existing report. [x] I checked whether this is already fixed on the dev branch or latest source. [x] I understand that maintainers want a well written issue before any code pull request.…”
“this is correct, the three backticks make a proper code block and the language hint is just for syntax highlighting”
“one thing I underestimated with MiniMax H3 is how much the prompt structure matters. for video editing, simply saying“replace this person with this reference” leaves way too much open to interpretation. the more reliable approach is to tell H3 very clearly what each input is responsible for, then separate the things you want changed from the things you want preserved. A compact version looks like this: subject_definitions: <Video 1> is the source video. Its camera movement, framing, enviro”
“I'm a beginner learning more about prompting and web development, and I decided to build a small tool to help me structure prompts instead of writing everything from scratch every time. I built it mainly as a learning project, so I want to be upfront: it is not a finished or professional product, and there are probably things that don't work as intended. The basic idea is to make prompt construction more structured. The tool is called NEON//CONTEXT and currently includes: Context Builder”
“What I wanted To offer a handful of MCP prompts beside the generated tools. The tools are projected from a GraphQL schema, so their descriptions can say what a field is and never what the application does with it — decorate buys back a line or two per tool, but a line is not room to explain a domain model. A prompt is exactly the right shape for that: fetched deliberately, once, and it is the one place a client can be handed the model rather than the schema. It is also what a user facing client”
“https://gemini.google.com/gem/1Coj3RzT1goCBegos-tiBFUzj_lUresY8?usp=sharing This is an interactive system that guides you step by step in creating the perfect prompt. You don't need to be an AI expert: the system asks the right questions, generates dynamic options tailored to your context, and transforms your ideas into a structured, safe, and immediately usable prompt. All in just a few minutes, with the highest quality. submitted by /u/ZioGino71 [link] [comments]”
“Clear testing brief. I’d ask testers to run the same one-sentence idea twice—once through your structured workflow and once directly in Suno or Udio—then compare which result followed the intended style and exclusions more closely. That gives you a cleaner signal than asking whether the generated song was simply “good.””
“UPDATE — I’ve officially named the framework: PROMPTOPSY. Stop rewriting. Find what actually broke. PROMPTOPSY is an AI prompt debugger powered by TASKSPEC Engine 3.4.2. Most prompt tools assume the wording is the problem and immediately rewrite it. PROMPTOPSY does the opposite: it first tries to diagnose what actually failed — wording, missing context, conflicting instructions, vague success criteria, the wrong model/tool, weak evidence, or the workflow itself — and then makes the smallest repa”
“I’ve been doing a lot of image-to-prompt experiments lately, and I realized I kept rewriting the same instructions depending on what kind of image I was analyzing. So I put together a conclusion. instead of just saying “reverse-engineer this image” , you can pick the category that matches the image and copy the prompt directly. might be useful if you often use ChatGPT or other multimodal models to analyze references. 1. General image → prompt works for pretty much anything. Prompt: Reverse-engin”
“Honestly, continuing writing, but do reach out to the dev and make a suggestion to implement a feature (probably best on their GitHub, as a feature request) that might address having a option to chose what gets open by default (note or profile)”
“I use this prompt when I’m sick of ai generated content and want it to sound like what I speak like. Take the given text and make it formatted in UK english, casual heavy use of slang words and euphamisms, use the level of wording a 12 year old would understand, this is an anti-AI sounding rewrite, no perfect grammar NO EM DASHES!!!, use colloquial terms and words not in american english so much. Sources like the newspaper "The Sun" from the 90's and how they would word their stori”
My prompts were all over the place, split between obsidian, a bunch of notepad.exes, and VS Code pages. To fix this, I built an extension that lets you store and interact with…
“Hey [ r/PromptEngineering ]( r/PromptEngineering )!I have been building AI agent workflows and noticed that over 70% of prompt tokens in real-world use are pure bloat: conversational rambling, duplicate notes, unformatted transcripts, and redundant code context.Instead of paying for an LLM to clean prompts for another LLM (adding latency and cost), I built lcc (Local Context Compiler). It is a fast, local-first Python/TypeScript CLI that runs in <15ms with zero external API calls:- Cleans and”
“I'm using Groq API with the Qwen model. I'm trying to create unique prompts that I'll later feed it to an Image Generation API. I asked AI to write me a random prompt to be used for image generation, it must include character name and be a food theme. It's giving me repetitive answers. I was thinking of make it read a csv file to check if prompts exist but that wouldn't work over time since the csv will grow large and reach the token limit. Is there any other methods to make”
“clever idea, the determinstic dedupe is way more useful than people realize until they start tracking token waste”
“Testing the model means hand writing a curl body every time, which is why it took three attempts to notice the prompt was not being built properly. Want a small CLI. A REPL for poking at it, and a one shot mode for a single question. No retrieval in it yet, this is just to prove the stack works before anything gets built on top.”
“This is super helpful, thank you. Really good callout on what happens after the plan is generated. It’s fully interactive, you can complete tasks, edit things, add/remove items, and ask the tool to generate more tasks or just restructure. And the “reset tool” framing is interesting too. A big part of what I’m trying to solve is exactly those moments where everything is piling up and you just need somewhere to dump it all and figure out what actually matters. Appreciate you taking the time to loo”
Feature Request: Queue and Auto-Retry for Document Generation
“My problem was consistency. I'd feed the model rough bullets and get a different format every single time, so I could never reuse the output. What worked was treating the prompt like a document generator with a fixed template baked in, then handing it only the raw content. ``` Act as a document generator. Always output in THIS structure, nothing else: # [Title] **Purpose:** one sentence on why this doc exists **Audience:** who reads it ## Context 2-4 sentences of background. ## Key points Bu”
“i am just tired of looking for a reliable open-source model AI API with ZDR and don't even save my prompts. I'm thinking of a service for people like me who want to use it.idea is a simple OpenAI-compatible API:Access to open-weight modelszero retention of prompts and completionsno training on dataonly retain metadata required for billing and operations: request ID, model, input/output token counts, latency, timestamp, etc.no request/response in logsJust here to check interest. I am not 100% sur”
“Problem Aspire currently ships with a set of skills that are useful for locally running agents. These are capable of reading the docs through the CLI and providing very relevant information about Aspire. Integrating similar functionality directly into the website would allow those without funds or access to LLMs to ask questions without flooding the various support channels. Desired Solution When visiting the documentation at aspire.dev/docs, it would be nice to have a means of calling up a chat”
“Ported a prompt that worked great on a frontier model over to a smaller open model and it just... didn't hold up. Same instructions, same structure, noticeably worse output. Took a while to realize it wasn't really a prompting problem. Frontier models are forgiving of imperfect context, they infer around gaps well. Smaller models punish that same imperfection hard, so a prompt that "worked" before was actually being propped up by the model quietly compensating for weak retrieva”
“Your nodes work exactly how I want, thank you so much! I have more than 20 prompts saved in one list, and it would be nice to have them sorted alphabetically. It could be a function on the manager node. I did export the list and sort them in VS code, but having it built in would be a nice feature.”
“[ChatGPT] ★★★ 3/5 (v1.2026.224) — It gets sidetracked and changes plans half way through a project it should first make sure it understands the prompt before it assumes what direction to go. I would rather it try to create a better initial prompt.”
“Check Existing Issues [x] I have searched for any existing and/or related issues. [x] I have searched for any existing and/or related discussions. [x] I have also searched in the CLOSED issues AND CLOSED discussions and found no related items (your issue might already be addressed on the development branch!). [x] I have checked whether this issue is already fixed on the dev branch or in the latest source. [x] I am using the latest version of Open WebUI. Installation Method Git Clone dev checkout”
“https://promptbasepro.lovable.app - an AI prompt library for freelancers to store their prompt. It also had improve features so that the prompt can improve at the same time”
“Have you ever tested any Claude projects and prompts? I have one that I just posted about - https://www.reddit.com/r/alphaandbetausers/s/5XdgrTNdER”
“My problem was consistency. I'd feed the model rough bullets and get a different format every single time, so I could never reuse the output. What worked was treating the prompt like a document generator with a fixed template baked in, then handing it only the raw content. ``` Act as a document generator. Always output in THIS structure, nothing else: # [Title] **Purpose:** one sentence on why this doc exists **Audience:** who reads it ## Context 2-4 sentences of background. ## Key points Bu”
“Submission checklist [x] This is a bug, not a usage question. [x] I added a clear and descriptive title that summarizes this issue. [x] I used the GitHub search to find a similar question and didn't find it. [x] I am sure that this is a bug in LangChain rather than my code. [x] The bug is not resolved by updating to the latest stable version of LangChain (or the specific integration package). [x] This is not related to the langchain community package. [x] I posted a self contained, minimal, repr”
“If you maintain good prompts, you know the pain: your library lives in one platform (or a sad Google Doc), and every other AI starts from zero. In the v3.0 we launch Sunday, your prompt library and multi-step prompt chains follow you across ChatGPT, Claude, Gemini and Grok - one library, everywhere, plus slash-trigger insertion so a saved prompt is two keystrokes away in any of them. The rest of 3.0: one search box across all four platforms (from any tab), one folder tree spanning them, cross-pl”
“When I paste a block of text and build slides in one shot, the model guesses my intent and usually guesses wrong. Splitting it into two steps fixed most of the rework. Step one, make it plan before it writes: ``` Here is some text. Do NOT make slides yet. """ [paste text] """ First, tell me: 1. What is the single goal of this deck (inform, persuade, teach, update)? 2. Who is the audience and what do they already know? 3. What are the 5 to 7 distinct points worth the”
“Dows it have a prompt optimizing harness or tool chain or rubric that scores against various models? For example Are you able to download a specific prompt that is optimized for Opus-5 vs say Opus-4.8 or gpt-5.5 that will shape the front matter for the appropriate model? Maybe json schemas that build the prompts based on model and harness?”
“Submission checklist [x] This is a bug, not a usage question. [x] I added a clear and descriptive title that summarizes this issue. [x] I used the GitHub search to find a similar question and didn't find it. [x] I am sure that this is a bug in LangChain rather than my code. [x] The bug is not resolved by updating to the latest stable version of LangChain (or the specific integration package). [x] This is not related to the langchain community package. [x] I posted a self contained, minimal, repr”
good suggestion, it usually catches the obvious stuff at least
“Preflight Checklist [x] I have searched existing requests and this feature hasn't been requested yet [x] This is a single feature request (not multiple features) Problem Statement Claude Code session transcripts already store full model reasoning in type: "thinking" blocks, separate from the visible reply. There's no built in way to search, reference, or reason over that content later it's only accessible by digging into the raw JSONL yourself. I ran into this building a personal nightly memory”
“One thing I've always found surprising about "harnessess" (god I hate that word) like Pi or Opencode is the lack of a customizeable system prompt. I can understand it for closed source ones, but open ones?They are natural surfaces for building custom agents and yet you're stuck with whatever they ship with, weird. It's not like it's too complicated api-wise either.There must be something I ignore.”
“I have one general prompt that I use across different projects, with extra project-specific prompts where needed. Each project also has explanation documents that tell the AI how the different parts currently work and fit together. The big time saver is a program I "made" that can apply the AI’s code changes and update those explanation documents at the same time. I used to spend a lot of time manually keeping everything in sync, whereas now most of that is handled for me. What’s surpr”
“Thank you for the feedback! Glad to know few tools clicked with you. Yeah the AI prompt builder was built because documentation was a huge headache, so I created a set of prompts based on all my experiments with different LLMs, what works and what doesn't. I've included every single thing to help the agent one-shot the output, I'll keep updating it as well. I'll monitor it's usage for a while and trim it out. Thank you❤️”
“If Claude or ChatGPT keeps giving you vague answers, try structuring your prompt like this: 1. ROLE Tell it who it should act as. “You are a senior TypeScript developer who writes clean, production-ready code.” 2. CONTEXT Explain what you’re working on, your tech stack, the problem and any limits. “I’m building a Next.js dashboard. The login page works, but users aren’t redirected after signing in. I’m using Supabase Auth and TypeScript.” 3. OBJECTIVE Say exactly what you want it to do and how y”
“The reason most AI content generator output reads generic is that a vague prompt gives the model permission to be safe. It hedges, it covers every angle, it says nothing that could be wrong. Safe writing is forgettable writing. I started constraining hard instead of describing. This is the frame: ``` Write [piece] under these constraints: - ONE claim. State it in the first two sentences. Everything else defends it. - Reader: [specific person] who already knows [X]. Do not explain [X] to them. -”
“Instead of going back forth with agy trying to perfect your prompts and having it change code you can really work out an idea then have it give you a detailed prompt. Do this inside a notebook in Gemini that has a bunch of sources like chats, spreadsheets, websites, images for the project. You can even share your codebase in that notebook. Use these instructions under your notebook settings.👇👇👍 You are my AI Technical Product Manager and Prompt Architect. I am a non-technical Project Manager.”
“Just wrote a new skill - reprompt. You pre or append your prompt with it when you think it might not be the best. Then the agent does not run your prompt, but instead rewrites it 3 ways to make it better. Then you pick the best one to submit. submitted by /u/MirafoldHQ [link] [comments]”
“I've been prototyping a format for ebooks about AI prompting where, instead of just reading text, you click through exercises that build directly in the page — like assembling a prompt piece by piece and watching it update live. Before I go further with it, I'm trying to figure out: Does something like this already exist that I should look at? Would you actually use something interactive like this, or is reading still preferred for this kind of content? What would make you NOT trust/buy”
“I did go to the source. Your own lmxdi repo calls itself a collection of prompts, and your own MogriBot description says it is a Mogri prompt + definitions + style tweaks. The “runtime” source I found is things like MOGRI=minC(I|A|!D|preE) , Δ->REVERT , and DR:qs(eat,loc,ID,eater) . Those are semantic instructions that the underlying LLM interprets. I cannot find an independent interpreter or execution mechanism implementing those tokens. Your anti-drift/object-preservation test is potentiall”
“Yesterday I asked how people actually manage their AI prompts, and the discussion got more interesting than I expected. A few problems kept coming up: • Finding a prompt you created months ago • Keeping track of different versions • Knowing what changed when a prompt stops working • Keeping a large prompt collection organized So I'm curious: If you had to design the ideal solution for these problems, what would it look like? Would you prefer: A) Folders + tags + search B) Automatic version h”
“I like to know what framework you use to prompt.Share Your Framework and let's discuss about it. submitted by /u/abhinavshabu- [link] [comments]”
“For the first couple months I kept my prompts in a Google Doc. Version A, version A-final, version A-final-2, you know the drill. Worked until it didn't. The moment it broke: I tweaked a production prompt to shave some tokens, shipped it, and the output quality quietly dropped. No error. No alert. I found out three days later when a support ticket came in about garbage responses. The prompt still "worked," it just worked worse, and nothing told me. Prompts rot the same way code doe”
“I'm starting to believe that keeping production prompts in the codebase is one of those decisions that feels harmles until you have to explain a quality drop. At the moment, we’ve got prompts scattered all over the place. Some are in config files, some live in helper functions and a few are buried who knows where. Then someone tweaks the prompt, someone else changes the model and a few days later the workflow starts behaving differently. Half the investigation is just figuring out what chang”
“Hi everybody! I noticed that a lot of repos are implementing similar local prompt management systems over and over again. Instead of solving the same problem repeatedly, I decided to create a solution that anyone can use. I created pf for this... Prompts live as files in your repository, versioned Git-style (commit, diff, rollback), and everything works fully offline—no account, no cloud, nothing to sign up for. Repo: https://github.com/tursdev-org/promptflip Just run: pip install promptflip Des”
“That’s really helpful, thank you. The “forgetting to save context mid-flow” problem is exactly the kind of friction I want to eliminate. The goal is to make it feel less like manually managing memory and more like a quick, natural action inside the existing workflow. One click at most, with clear control over what gets saved. I’m also exploring ways to make saving context easier without silently capturing everything. Privacy and convenience have to work together here.”
“What it is: sits in the ChatGPT/Claude/Gemini/Perplexity/Le Chat message box and scores your draft 0-100, with itemized findings (no output format specified, scope too broad, no fallback for when the model isn't sure, etc). Not a rewrite tool - it tells you what's wrong, you decide what to do about it. Stage: fully built, not on any extension store yet (mid-review on Chrome/Firefox), so it's a 2-minute sideload right now - no signup, no account on my end either. Install/download: htt”
“Oh, my verifier Whisper IS primed with the wakewords in the prompt. So I am breaking not one, but two of your rules! But without this, there are too many different spellings of Jarvis, Rhasspy, etc.”
“Intro So, I have been searching for a good simple and cheap (consumer-based) Prompt Management & Engineering tool, where I not only can store prompt, but also experiment and analyze on it. Most platforms were heavy & complex (developer-focused), and/or expensive. I wanted to improve prompts - not drain $200 down my wallet while configuring 5 .yaml files. So, I found the top 3-4 tools/platforms for this use-case/ICP and created a comparison table. Comparison Tool/Platform Affordability +”
“Prerequisites [x] I am running the latest code. Mention the version if possible as well. [x] I carefully followed the README.md. [x] I searched using keywords relevant to my issue to make sure that I am creating a new issue that is not already open (or closed). [x] I reviewed the Discussions, and have a new and useful enhancement to share. Feature Description It would be great if there was a way to have dynamic content in the llama server WebUI System Prompt. So that things such as {{ date }} co”