rlm-workflow streamlines AI development with sequential workflow
A new AI development tool, rlm-workflow, has been released, aiming to improve collaboration and efficiency in AI projects by separating information from chat and organizing it into a sequential workflow. The tool is based on a kanban workflow model, with each phase outputting a markdown document and taking the previous phase's documents as input. This approach is expected to reduce errors and improve reproducibility in AI development.
Key points
- rlm-workflow is a new AI development tool that streamlines collaboration and efficiency in AI projects
- The tool separates information from chat and organizes it into a sequential workflow based on a kanban model
- Each phase of the workflow outputs a markdown document and takes the previous phase's documents as input
- The approach is expected to reduce errors and improve reproducibility in AI development
- rlm-workflow is open-source and available on GitHub
rlm-workflow is an open-source tool designed to improve collaboration and efficiency in AI development. It achieves this by separating information from chat and organizing it into a sequential workflow based on a kanban model. This approach is expected to reduce errors and improve reproducibility in AI development.
The tool is modeled after a regular kanban workflow from requirement to implementation plan to testing and manual QA. Each phase of the workflow outputs a markdown document and takes the previous phase's documents as input. This ensures that each phase is gated on fulfilling criteria defined in the previous phase, and at the end of a phase, its output documents are locked.
rlm-workflow is open-source and available on GitHub. Its release follows the publication of a paper on recursive language models, which demonstrated a method of increasing effective context length to 10M tokens by using sub-agents to move information from the context window to an information store outside the chat.
Sources
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