Provides an open-source platform for prompt engineering, enabling teams to build, evaluate, and refine production-grade LLM applications. It centralizes prompt management, supports batch evaluations, and offers automated improvements based on performance metrics, reducing iteration time and improving output quality.
Funding
$160K raised to dateRaised to date based on public sources. This may differ from the amount the company actually raised and is based only on what is publicly available on the internet.
Founders
Product
Problem
Building production-grade Large Language Model (LLM) applications requires significant prompt engineering effort, including prompt creation, testing, and refinement, which can be time-consuming and lack collaboration. Existing solutions often lack centralized management, version control, and automated evaluation capabilities, hindering the development of high-quality LLM outputs.
Solution
Latitude is an open-source prompt engineering platform designed to streamline the development of production-ready LLM applications. It provides a centralized hub for prompt management, enabling teams to collaboratively write, iterate, and version prompts. The platform offers a playground for testing prompts at scale, along with tools for logging and debugging requests. Latitude supports automated evaluations using LLM-as-judge, code, or human feedback, and includes a refiner that automatically suggests prompt improvements based on evaluation results. By centralizing prompt engineering workflows and automating key tasks, Latitude reduces iteration time and improves the quality of LLM application outputs.
Target Audience
Latitude is designed for developers and teams building production-grade LLM applications who need a collaborative, centralized platform for prompt engineering, testing, and optimization.
Features
- Centralized prompt manager for team collaboration, version control, and rapid iteration
- Dynamic prompts using template syntax for building complex flows
- Playground for testing prompts at scale with production or synthetic logs
- Observability tools for logging and debugging requests
- Automated evaluations using LLM-as-judge, code, or human feedback
- Dataset creation and upload for batch testing of prompts and evaluations
- Refiner that automatically improves prompts based on evaluation results
- Support for integrations via MCP servers