Demo of the generator-evaluator workflow.
Generate legal documents with case data
Qwen-Qwen2.5-Coder-32B-Instruct
Generate a novel step-by-step
Generate code based on user prompts
Generate web application code from descriptions
Generate an interactive app interface
Generate code solutions to specific problems
Find and generate job application documents
Generate Python code with revisions and error checks
Generate interactive web apps
Generate code solutions with prompts
The Generator-Evaluator Workflow is a dynamic tool designed to streamline content generation and evaluation. It combines generative AI with evaluation metrics to create and refine outputs based on specific criteria. This workflow is ideal for tasks like writing, problem-solving, or creative projects where iteration and improvement are essential.
β’ Dual-engine approach: Combines a generator for content creation and an evaluator for quality assessment.
β’ Customizable criteria: Define your own evaluation metrics to tailor outputs to your needs.
β’ Iterative refinement: Easily refine generated content based on evaluation feedback.
β’ Performance tracking: Monitor improvements in content quality over iterations.
β’ User-friendly interface: Simplifies the process of generating, evaluating, and refining content.
β’ Collaborative capabilities: Share and work on workflows with team members.
β’ Version control: Keep track of different versions of generated content for comparison.
What happens if the generated content doesnβt meet my criteria?
If the generated content doesnβt meet your criteria, you can refine it by adjusting the input parameters or criteria and regenerate the content. The evaluator will reassess it based on the updated criteria.
How do I customize the evaluation metrics?
You can customize the evaluation metrics by selecting or inputting specific criteria in the toolβs settings. These criteria will guide the evaluator in assessing the generated content.
Can I use this workflow for non-text content?
While the workflow is primarily designed for text-based content, it can also be adapted for other types of tasks, such as reviewing structured data or evaluating problem-solving approaches, depending on the customization options available.