Promposer.AI
Recently I found myself spending too much time testing edge cases when building LLM agents. Re-running the same case over and over again really kills the vibe and takes the fun out of building. As Andrej Karpathy (and others) have said, it should really be called “context engineering” rather than prompt engineering. Writing instructions is the easy part. The hard part is providing the right context and tools while staying within context window limits. And evaluation is the slowest step. That’s the main purpose of Promposer: to handle evaluations for you, so you can focus on improving prompts…
Promposer.AI launched on September 3, 2025 with 10 votes, #1,170 of 8,160 launches that month and more than 50% of that year's launches. The site was still online in September 2026, 1 year on.
What it does
Write and iterate on prompts, add context/tools, and run structured test cases to simulate edge situations. Compare outputs across models, review threads in realtime via API, and catch production issues early. Works in your IDE, browser, or pipeline.
Craft better prompts. Get better results. Compose, improve and evaluate LLM prompts.
Design comprehensive test scenarios and tools to evaluate your prompt's performance across different AI models. Paste a previous conversation transcript to generate a relevant test case. Define context definitions that can be used acrossfrom promposer.ai
In the maker’s words, at launch
Recently I found myself spending too much time testing edge cases when building LLM agents. Re-running the same case over and over again really kills the vibe and takes the fun out of building. As Andrej Karpathy (and others) have said, it should really be called “context engineering” rather than prompt engineering.…
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Prompt-Engineering Tool: AI-to-AI Testing for LLM2023 · github.com · ▲37Spelltest framework simulates conversations between AI ‘synthetic users' in an environment to test and refine LLM-based applications. It ensures your app converse with utmost accuracy and relevance. Post-chat, Spelltest assesses responses, providing qualitative and quantitative feedback on performance. Suitable for both chat and completion modes. When to use: - After modifying your prompt. - When your LLM provider updates. - As a CI step for you repo. All feedback and collaborations appreciated!
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