reddit · SideProject · 09-15 21:50 54 分 ← 返回列表

I built a tool that lets you manage your prompts and deploy them without triggering deployment pipeline.

So here, people who are building backend systems that use prompts and LLM calls for intelligence operations in their backend, they encounter two major problems: They don't know how to write structured prompts that give high-quality output and accurate results. They treat prompts as string literals, and changing a single word leads to Commit -> PR -> Review -> server re-deploy I built a solution, Prompt Engine What this tool is capable of and Use cases- A kitche

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痛点：LLM 后端把 prompt 当字符串硬编码，改一个词就要走 Commit→PR→Review→重新部署，且缺少高质量结构化 prompt 的编写、版本管理和回滚能力。

目标用户：使用 LLM/prompt 的后端工程师、AI 应用开发团队、平台工程/MLOps 团队。

证据：They treat prompts as string literals, and changing a single word leads to Commit -> PR -> Review -> server re-deploy. ... The prompt in your backend is replaced by a single API call, which gives you the active prompt of that engine. ... no deployment needed. ... you can ROLLBACK. ... My own office started using it

变现：SaaS 订阅：按项目数、prompt 版本数、API 调用量或团队席位收费；提供免费层，付费解锁团队协作、审计、回滚历史、私有化部署和企业 SSO。

MVP：2 周内做一个极简 prompt registry：Web 仪表盘管理 prompt 版本，提供 API 按 key 返回 active prompt，支持草稿/发布/一键回滚和时间线；先不做自动优化 prompt，只解决免部署更新和版本回滚。

竞品：PromptLayer、Langfuse、Humanloop、LangSmith、Helicone、Portkey、Agenta、Promptfoo

摘要：发帖人发布 Prompt Engine，主打 prompt 版本管理、免部署 API 调用、回滚和自动优化，并称自己办公室已在用。它切中 LLM 后端把 prompt 硬编码导致改词就要走完整部署流程的痛点，但该赛道已有 PromptLayer、Langfuse、LangSmith 等大量竞品，且帖中没有真实付费或收入数据。