[RFC] Roadmap to v0.1

[heyjustinai]

llama-prompt-ops v0.1 Roadmap

Hi everyone,

We're excited to share the proposed roadmap for llama-prompt-ops v0.1! Our goal is to make:

1. prompt optimization for LLM easy, fast and reliable. no more endless trial and error.
2. enabling prompt adaptation for different model

Below is a list of planned features and improvements for the v0.1 release. Your feedback is invaluable—please let us know what you think, and suggest anything else you'd like to see!

Proposed Features

1. Improved Logging and Transparency

Problem: Current logging is minimal, making it hard to debug or understand optimization steps.
Solution:

• Add detailed, user-friendly logs for each stage of the optimization pipeline.
• Include metrics and intermediate results for better traceability.
• Example usage:

llama-prompt-ops migrate --log-level DEBUG

h/t to @siddhantparadox for starting this.

2. One-Shot Enhancer

Problem: Existing optimization requires multiple iterations or large datasets.
Solution:

• Develop a "one-shot" proposer that can suggest prompts enhancement from a single example or minimal data.
• Useful for rapid prototyping and low-data scenarios.
• Example usage:

llama-prompt-ops enhance <prompt>

3. Prompt Lineage Tracking

Problem: It's difficult to track how a prompt has evolved or which changes led to improvements.
Solution:

• Implement prompt versioning and lineage tracking.
• Store metadata and diffs for each optimization step.
• Example usage:

llama-prompt-ops ops

4. Multi-Modality Support

Problem: Current system only optimizes text prompts; no support for image or multi-modal inputs.
Solution:

• Extend optimization to handle prompts involving images, tables, or other modalities.
• Enable evaluation and transformation of multi-modal datasets.
• Example usage:

llama-prompt-ops migrate --input-type multimodal

5. Llama-Prompt-Ops Web App

Problem: CLI-only workflow can be limiting for some users.
Solution:

• Develop a simple web app for interactive prompt optimization.
• Features include uploading datasets, configuring optimization, and visualizing results.
• Potentially a hosted solution reducing time to first success.
• Example usage:
  Access via http://localhost:8080 after running:

llama-prompt-ops web

The ask

• Do these features align with your needs?
• Are there other capabilities you’d like to see in v0.1?
• Would you be interested in helping test or develop any of these features?

Please comment below with your thoughts, suggestions, or questions. Your input will help us prioritize and refine the roadmap! Thanks for being part of the llama-prompt-ops community!

@jeffxtang @vjeux @raedle @LakshyAAAgrawal @ujjwalkarn @vermaMachineLearning @init27 @terencezhang1997 @CyrusNuevoDia @ammirsm

[EllAchE]

Things that would be interesting to me:

• Contributor guide
• Architecture diagram
• "Noob mode". If I want to put in minimal effort and make my prompt better can we support that? I.e. if I come with just a prompt and no training examples is there any way to still reason effectively and improve the performance of the prompt?
• more complex dataset structures - might be hard to abstract this but use cases beyond just single Q and single A could generalize this better

Some nice to haves

• limits on prompt output (i.e. max tokens for optimized prompt)
• support for non llama models :)