User : PromptQuorum

• Created: 1773912725
• Karma: 1
• I’ve been working on PromptQuorum, a prompt design and optimization tool for people who run LLMs locally (Ollama, LM Studio, etc.) and want more reliable results without giving up privacy.

  What it does PromptQuorum helps you:

  Turn a rough idea into a structured, multi-step prompt using established prompting patterns (chain-of-thought, role-based, checklists, etc.).

  Interactively refine prompts: the tool asks clarifying questions, suggests missing constraints, and lets you approve each change.

  Run the final prompt across multiple models / backends (local LLM, API LLMs, or your own keys) and compare outputs side by side.

  Create a “meta” summary of overlapping answers, so you can see where different models agree or disagree.

  The focus is on local-first workflows: you can point PromptQuorum at a local LLM (e.g. Ollama/LM Studio) and keep your data on your own machine.

  Problem / motivation Most “prompt engineering” UIs are optimized for single-shot API calls. They assume you want to hit OpenAI/Anthropic directly and they rarely support:

  Local-only setups that must not send data to external APIs.

  Iterative prompt construction with explicit user control over each step.

  Comparing how different models (local + remote) respond to the same prompt in one place.

  As a result, people who care about privacy or who run local LLM stacks end up with a mix of shell scripts, ad‑hoc Markdown files, and screenshots. It works, but it is fragile and hard to reuse or teach to others.

  I’m building PromptQuorum to make this more systematic: structured prompt design, controlled experiments, and better reuse of “good prompts”, especially in local environments.

  How it works (high level) You start with a rough, short task description (“help me design a pricing model for my SaaS”, “audit this Python script”, “generate test data”, etc.).

  PromptQuorum proposes a suitable prompting approach (e.g. multi-step, role-based expert, critique-and-rewrite) and shows you a draft structure.

  You go through an interactive flow: the tool asks for missing details (audience, constraints, style, edge cases) and updates the prompt template.

  Once you approve the final prompt, you can:

  Send it to a local LLM (Ollama / LM Studio)

  Send it to one or more remote APIs (either via built-in credits or your own keys)

  The outputs are shown side by side, and you can mark “good” results, tweak the prompt, and re-run.

  Finally, you can generate a “consensus” or “meta” summary over selected answers (e.g. to see what 3 different models agree on).

  What’s different Some specific design choices that might interest this crowd:

  Local-first by design:

  Native support for local LLMs and the option to stay fully offline for sensitive data.

  Remote APIs are optional and pluggable (bring-your-own-keys if you like).

  Prompt process rather than just a text box:

  Instead of a single big textarea, PromptQuorum treats prompt design as a small workflow (collecting requirements, choosing a pattern, adding tests, etc.).

  It keeps an explicit “prompt history” so you can see how the prompt evolved and roll back changes.

  Teaching layer:

  Built-in mini-guides and examples of good prompts, targeted at people who are technical but not prompt experts.

  The idea is that you learn prompting patterns as you use the tool, not from a separate course.

  Multi-model “quorum” view:

  You can run a prompt against multiple models and then generate a synthetic comparison or consensus summary.

  This is meant for people who want to check robustness or see whether a local model is “good enough” vs a remote one for a specific workflow.

  Who it’s for People running Ollama, LM Studio, or other local LLM stacks who want better prompts and repeatable workflows.

  Engineers building internal tools where data cannot leave the organization.

  Power users who frequently compare different models and want a cleaner workflow than a dozen browser tabs.

  What I’d love feedback on A few specific questions for HN:

  Does the “interactive prompt design” flow sound actually useful, or would you prefer a more minimal “power user” UI?

  For local-first users, what’s the most important integration: Ollama, LM Studio, Open WebUI, LangChain, something else?

  Which parts would you want to script/automate (CLI, config files, API) vs do in the UI?

  If you’re interested, sign up waitlist on www.promptquorum.com

  I’d really appreciate any feedback, especially around:

  Gaps vs how you currently design and test prompts.

  Concerns about local/privacy, UX, and lock-in.

  Ideas for better ways to integrate with local LLM tooling you already use.

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