Operationalising Multi-Dimensional Evaluation for Conversational Agents Using GenAI Evaluation Pipeline

Operationalising multi-dimensional evaluation for conversational agents is crucial for assessing their effectiveness in retail environments. A recent study by Niranjan Kumar M and colleagues, submitted on July 13, 2026, introduces the GenAI Evaluation framework, designed to enhance the evaluation process of retail conversational systems.

Overview of the GenAI Evaluation Framework

The GenAI Evaluation framework offers a scalable, governed pipeline that addresses the challenges of traditional evaluation methods. It goes beyond simple lexical-overlap metrics to evaluate various dimensions such as intent alignment, factuality, helpfulness, clarity, and tone. This comprehensive approach ensures that conversational agents meet the necessary standards for quality interactions.

The pipeline processes production chatbot logs through a series of steps including normalization, sharding, and asynchronous execution. This systematic approach allows for schema-constrained LLM scoring, which is essential for maintaining consistency and reliability in evaluations.

Selective Re-evaluation and Governance

One of the unique features of the GenAI Evaluation framework is its selective re-evaluation process. This mechanism targets only those records that are incomplete, malformed, or schema-invalid, thereby optimizing resource use and ensuring thorough evaluation. The framework supports auditability through schema locking, versioned configurations, and validation logs, which provide traceability and accountability in the evaluation process.

By processing approximately 50,000 records daily, the framework has already evaluated over two million interactions. This large-scale evaluation not only enhances the quality of conversational agents but also contributes to the ongoing development of AI technologies in retail.

Results and Validation of the Evaluation Pipeline

The effectiveness of the GenAI Evaluation framework was validated using a dataset of 12,980 stratified-random human-labeled records from trained annotators. The classification process covered 14 intents, 156 sub-intents, 18 major domains, and 129 sub-domains. The results reflected a macro F1 score of 0.93 and an 89% human-acceptability accuracy for translation tasks, indicating a high level of performance in evaluating conversational agents.

This innovative evaluation framework not only improves the reliability of conversational agents but also sets a benchmark for future evaluations in the AI domain.