Yapay Zeka Aracı Kaynak Doğrulaması
Araç içeriklerinin eski veya pazarlama kaynaklı yanlış bilgiler taşımasını önler.
315+ test edilmiş prompt şablonu; yazıdan koda, görsel üretiminden kariyere kadar. Değişkenleri ({böyle}) kendinize göre doldurup doğrudan kullanın.
Araç içeriklerinin eski veya pazarlama kaynaklı yanlış bilgiler taşımasını önler.
Ajan araç hatalarını sonsuz döngüye veya gizli veri kaybına yol açmadan yönetir.
Karmaşık API şemasını model çağrılarına uygun, doğrulanabilir bir sözleşmeye çevirir.
I’ve been making AI videos more seriously this year, and at some point comparing polished demo reels stopped being that useful. I wanted to see where these tools actually fit once you’re trying to put together a real project. I used roughly the same kind of brief across them: short marketing content with a script, visuals, voiceover, subtitles, and a finished export. Obviously
starting to think having a good workflow matters way more than chasing the newest model every week. curious how everyone else is building their setup these days. are you sticking with one tool or mixing a few together depending on the project? anyone else hit this point where ai content creation was supposed to speed everything up but now half the job is fixing weird little thi
Looking for the best prompt to give ChatGPT, Gemini, Perplexity, or any AI tool that instructs it to act as a **prompt engineering professional**. I need it to generate robust, conversational, and production-ready prompts that sound like a real person thinking—not robotic or generic. What's the prompt that works best for this? Any recommendations? Thanks. submitted by /u/Ok-Sci
I paste a lot of screenshots into ChatGPT / Claude. Half the time it latches onto the wrong button, log line, or panel, then I have to explain too much via prompts. To save my time and get work done faster with AI & Screenshots, I made Lookhere to just make screenshot self-explanatory so I don't have to waste my energy in writing prompts. It’s a fast, distraction-free tool desi
Something I’ve been wondering about lately: When you use ChatGPT or Claude, do you actually write detailed prompts, or have you mostly moved toward just talking to it like a person? I feel like there are two very different ways of using these tools. One is: "Here's the context, here's exactly what I want, here's the format…" The other is basically opening voice mode and saying,
Been poking at this for a while and it worries me more than it should. everyone's rushing to give llm agents real access now, read my email, run this tool, push to the repo, book the thing. the demos look great. the part that gets skipped is that the agent takes instructions from whatever text it reads. so a prompt injection isn't just "make the chatbot say something rude" anym
I ran the whole set of 44 composable AI- and prompt-focused apps I just released on my website through my system and had it build out five real samples compositions that you can actually build today yourself, assuming you have the needed technical chops. Every one listed here uses only tools from the 44 and the system checked that the data actually flows; the port/typecheck/dec
Design background here. The request I get most is "can you make this into an infographic," attached to three paragraphs of dense text with no sense of what the one takeaway is. The tool is never the problem. The thinking that has to happen before the tool is the problem. So I wrote a prompt that does the structuring part, the part people skip. It doesn't design anything. It dec
Solo marketing consultant, tool-fatigued, I mostly lurk. But this one earned its keep, so here it is. The problem was never the design of my decks, it was that they read like a table of contents instead of an argument, and clients glaze over by slide four. This prompt turns the strategy doc into a deck built as a case, not a summary. ``` Role: You are a narrative editor for cli
Hey there, I just shared a project on GitHub: https://github.com/Shoko-official/Claude-Science-System-Prompts It contains system prompts, tool definitions, and skills used in the Claude Science workbench. Hope it's useful to some of you! Feel free to leave feedback or drop by! submitted by /u/Dry_Highlight7019 [link] [comments]
We’ve built two release systems for one support agent and now we’re struggling to figure out which prompt is actually in production. PMs tune the conversational parts in a playground. Engineers keep fallback text, tool instructions and safety rules in code. Both workflows are reasonable on their own. But, last week a hotfix changed the system prompt and unfortunately, the evalu
I worked with my system to create a suite of 44 free apps for AI work that you can find on my website. Hint- these tools are MAJORLY composable. You can have your AI system take a look at the collection to see how they can flow together and for suggestions on ways they can be used. Cairn — Keeps Git history alive across independent storage providers through priority-ordered clo
If you have built autonomous agents or multi-step tool-calling workflows with LLMs, you have likely run into the standard failure modes that break production agents: Premature Action Bias: The model fires off tool calls or answers the user before mapping out prerequisites and the logical order of operations. Fragile Error Handling: When an API call fails or returns unexpected d
I created this tool to help me test and evaluate different model responses: RouterDash. This allows me to compare models from OpenRouter, Groq and Cerebas. I used this to find the cheapest possible, high quality responses for another project. Fully client side, all data is stored in browser local storage for complete privacy. I recently added prompt templates and image attachme
I'm a professional who uses ChatGPT a lot. I think it's an awesome tool, but I require accuracy and was finding myself fighting with GPT more than my partner. The main frustrations were confident answers based on assumptions or stale information, and GPT saying it was “checking” or “investigating” something when the response had actually finished. I wanted it to be more comfort
Every "write in my voice" attempt I made used to be me listing adjectives. Casual but professional, confident not arrogant, and so on. The output always came back sounding like a brand guideline wrote it, because adjectives are not a style, they are a vibe. What actually works is making the tool extract the rules from your own samples first, then write against those rules. Two
I've been reusing prompts heavily across ChatGPT and Claude for about a year. The thing that finally made my library actually usable wasn't a better tool — it was one change in how I categorized things. (Full disclosure since it's relevant: I ended up building a small tool around exactly this workflow. Not going to link it in the post — happy to drop it in a comment if anyone w
I'm an ops analyst and I try way too many tools. Too many tabs open at any given moment. The problem with comparing AI tools on real work is that they all look great on a clean demo and fall apart on an actual messy export. So I stopped judging them on the answer and started forcing them to show the work first. This is the prompt I paste before I let any AI report generator tou
Junior product analyst at a fintech. Building the deck stopped being the hard part a while ago. The hard part is standing in the room when a VP asks the one question I did not think about. So before every review I run this on my own deck or summary. ``` You are a skeptical senior executive reviewing my analysis before I present it. Here is what I am presenting: {paste your key
if you're building anything multi-turn and not structuring prompts for caching, your bill is probably several times higher than it needs to be. this isn't a model choice or a retrieval trick, it's purely how you order the prompt. the mechanic: put everything static (system instructions, tool definitions, few-shot examples, anything that doesn't change turn to turn) at the front
I don’t know where to begin. submitted by /u/IAmAlwaysCorrect9326 [link] [comments]