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PromptL, a templating language designed for LLM prompting

Hacker News

PromptL, a templating language designed for LLM prompting

Hey HN! We just launched PromptL: a templating language built to simplify writing complex prompts for LLMs like GPT-4 and Claude. Why PromptL? Creating dynamic prompts for LLMs can get tricky, even with standardized APIs that use lists of messages and settings. While these formats are consistent, building complex interactions with custom logic or branching paths can quickly become repetitive and hard to manage as prompts grow. PromptL steps in to make this simple. It allows you to define and manage LLM conversations in a readable, single-file format, with support for control flow and chaining, while maintaining compatibility with any LLM API. Key Features - Role-Based Structure: Define prompts with roles (user, system, assistant) for organized conversations. - Control Flow: Add logic with if/else and loops for dynamic, responsive prompts. - Chaining Support: Seamlessly link prompts to build multi-step workflows. - Reusable Templates: Modularize prompts for easy reuse across projects. PromptL compiles into a format compatible with any LLM API, making integration straightforward. We created PromptL to make prompt engineering accessible to everyone, not just technical users. It offers a readable, high-level syntax for defining prompts, so you can build complex conversations without wrestling with JSON or extra code. With PromptL, even non-technical users can create advanced prompt flows, while developers benefit from reusable templates and a simple integration process. We’d love to hear your thoughts!

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Indie HackersFits the IH revenue-focused audience · Strong signals: created, compatible · Missing: supports, reddit linkedin, podcasting
96%96% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.
best fitHighest predicted score across all platforms for this description.
Product HuntOn track for Day 1 leaderboard · Strong signals: claude, user, single · Missing: mac, agents, macos
91%91% predicted probability of success on Product Hunt, based on ML models trained on real launch data.
AppSumoStrong fit for a featured deal · Strong signals: users · Missing: plus, platform, intuitive
51%51% predicted probability of success on AppSumo, based on ML models trained on real launch data.
Hacker NewsMay not resonate with HN audience · Strong signals: io · Missing: https docs, excited, just released
44%44% predicted probability of success on Hacker News, based on ML models trained on real launch data.
nativeThis product was originally launched on this platform.
TrustMRRLess likely to generate early MRR · Strong signals: users · Missing: mobile apps, ios, personal
41%41% predicted probability of success on TrustMRR, based on ML models trained on real launch data.
Acquire.comPre-revenue stage for this audience · Missing: arr, mrr, revenue
14%14% predicted probability of success on Acquire.com, based on ML models trained on real launch data.
BetaListMay not resonate with beta-testers · Missing: web3, chat, crypto
0%0% predicted probability of success on BetaList, based on ML models trained on real launch data.

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