Prompting LLMs falls into two extremes: at one end is "vibe prompting," which relies on intuition and trial and error until something feels right. At the end are advanced tools that require deep technical expertise and follow rigid, linear processes that slow iteration. Without a structured way to test, compare, and refine, results remain inconsistent—especially at scale.
This prototype brings systematic evaluation to prompt engineering, making model behavior predictable and adaptable.
How it works
1.
Compare Prompts Side by Side
Review results in a single view to identify inconsistencies between prompts, models, and sample sizes.
2.
Generate & Annotate Eval Sets
Let LLMs suggest samples that experts can refine with minimal effort.
3.
Refine Iteratively
Identify patterns in errors and adjust prompts accordingly.
4.
Optimize Automatically
Use DSPy for automated prompt engineering, where AI iteratively refines prompts to reproduce user-defined input/output samples.
5.
Know When to Fine-Tune
When automated prompt engineering reaches its limits, this process helps transition toward model fine-tuning with already existent datasets.
This exploration aims to bridge the gap between prompt engineering and model alignment, transforming AI behavior from unpredictable to systematically improvable.