AI model optimization with human preference.
Improve AI model quality by testing the prompts, checkpoints, workflows, and inference settings around the model. HeyBee finds which configuration performs best and what to test next.
Start freeGet more from the model you already have.
Same weights. Different settings. Same prompt. Very different results.
Choose. Generate. Vote. Analyze. Repeat, until one configuration wins with confidence.
- Choose
- HeyBee selects the most informative prompt, checkpoint, workflow, or generation configuration to test next.
- Generate
- Your hardware produces matched outputs from the selected configuration.
- Vote
- People compare two outputs made from the same input and choose which has better quality.
- Analyze
- HeyBee turns each choice into preference evidence and updates what it tests next.
Two paths to better quality.
Optimize how an existing model runs, or use human preference evidence to improve the model through your training system.
Quality optimization without retraining
Improve outputs from the model you already have by optimizing prompts, checkpoint choice, generation settings, inference parameters, and complete workflows.
Model improvement through preference data
Collect reproducible human preferences, keep the comparison context and exclusions, and export the evidence to your RLHF or DPO training system.
AI model optimization, answered.
The short answers about model enhancement, output quality, and continuous improvement.
Start freeWhat is AI model optimization?
AI model optimization improves the quality, efficiency, or reliability of an AI system. HeyBee focuses on output quality: it uses human preference to optimize prompts, checkpoints, workflows, and inference settings, or to produce evidence for model training.
How does HeyBee improve AI model quality?
HeyBee tests matched outputs, collects controlled human choices, analyzes which preferences repeat, and selects the most informative configuration to test next. The loop continues until one configuration wins with enough confidence.
Does HeyBee retrain or change the model?
Not by itself. HeyBee can improve output quality without changing model weights by optimizing how the model runs. When weight updates are the goal, HeyBee exports preference data for your RLHF or DPO training system.
What parts of an AI system can HeyBee optimize?
You can optimize prompts, model or checkpoint choice, generation settings, inference parameters, and complete workflows across text, image, audio, or video outputs.
How is optimization different from evaluation?
Evaluation measures which option performs better. Optimization uses that evidence to decide what to test or change next. HeyBee supports both in one controlled human-feedback loop.
Can HeyBee support continuous model improvement?
Yes. Your generator can stay connected while HeyBee chooses new configurations, collects more votes, and updates the evidence. Your training system can also use HeyBee preference exports for repeated RLHF or DPO rounds.