Generative AI Evaluation Pipeline Selection Without Manual Configuration
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Evaluating generative AI systems is challenging due to inconsistent outputs for the same prompt, unreliable customer feedback, and the complexity and cost of ad hoc evaluation pipelines, which are difficult to author and require extensive manual configuration.
Innovation Solution
A user interface allows engineers to specify evaluation parameters without coding, leveraging a pipeline selection system to automatically select and execute evaluation pipelines, including data extraction, processing, and metric calculation, with results displayed in dashboards or comparative formats.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If ad hoc evaluation pipelines are used to evaluate generative AI systems, then evaluation flexibility is achieved, but device complexity and difficulty of authoring increase significantly
Solution Approach 1:
The evaluation system is segmented into separate evaluation pipelines that can be independently selected and combined. Each pipeline handles specific evaluation tasks (data extraction, processing, metric calculation), allowing flexibility without requiring users to author complex custom pipelines from scratch.
Solution Approach 2:
A pipeline selection system acts as an intermediary between the user and the evaluation execution. This mediator automatically selects appropriate evaluation pipelines based on user inputs, abstracting away the complexity of pipeline authoring and configuration while maintaining evaluation flexibility.
2Adaptability or versatility
If manual configuration of evaluation pipelines is performed, then evaluation customization is achieved, but loss of time increases due to extensive manual configuration required
Solution Approach 1:
The pipeline selection system performs self-service by automatically selecting and configuring evaluation pipelines based on user inputs. The system autonomously handles pipeline instantiation, parameter configuration, and execution setup, eliminating the need for users to manually configure complex pipeline parameters.
Solution Approach 2:
Evaluation pipelines are pre-configured and prepared in advance with standardized templates and parameters. This preliminary action allows users to simply select pre-built pipelines rather than configuring them from scratch, significantly reducing setup time while maintaining customization capabilities through parameter selection.
3Ease of operation
If existing evaluation methods relying on customer feedback are used, then evaluation simplicity is maintained, but reliability decreases due to unreliable customer feedback
Solution Approach 1:
The system replaces unreliable mechanical feedback collection methods with automated evaluation pipelines that use structured data extraction and processing. Instead of relying on subjective customer feedback, the system uses programmatically extracted data and standardized metrics to evaluate model performance, improving reliability while maintaining operational simplicity.
Solution Approach 2:
The evaluation system implements automated feedback loops where evaluation results are systematically collected, processed, and used to assess model performance. This structured feedback mechanism replaces unreliable customer feedback with consistent, measurable evaluation data that can be objectively analyzed and acted upon.
Data Source
AI summary
An evaluation request user interface display is generated for interaction by a user. The user interacts with the evaluation request user interface display to specify evaluation parameters. An evaluation pipeline selection system selects an evaluation pipeline based upon the selected evaluation parameters. The evaluation pipeline processes the evaluation parameters and submits evaluation data to an LLM to obtain evaluation results. The evaluation pipeline outputs the valuation results for interaction by the user who initiated the evaluation request.