LLM Evaluation Pipeline -- RAG, Summarization, Classification

The challenge

A technology-driven organization aimed to improve the reliability and performance of Large Language Model (LLM) applications by implementing an automated evaluation pipeline for tasks such as Retrieval-Augmented Generation (RAG), summarization, classification, and sentiment analysis. The objective was to establish a standardized evaluation framework capable of measuring model quality across both online and offline environments. The solution was designed to support flexible evaluation strategies using multiple frameworks, metrics, feedback modes, and ground-truth datasets to generate structured performance reports.

Key challenges

• Measuring LLM performance consistently across different task categories
• Managing evaluation across online and offline execution modes
• Integrating multiple evaluation frameworks and scoring methodologies
• Handling feedback collection for subjective and human-based assessments
• Maintaining scalability for evolving LLM tasks and enterprise requirements

Technology used

• Programming Language: Python
• Evaluation Frameworks: RAGAS, DeepEval, G-Eval, and OpenAI Evals for LLM benchmarking
• Models Evaluated: GPT-based models and Hugging Face transformers
• Storage/Data: CSV datasets for ground truth validation and OpenAI APIs for evaluation workflows
• Evaluation Modes: Online and offline performance testing with configurable metrics

What we built

• Input Configuration: Accepts user-defined evaluation settings such as task type, model, and evaluation mode
• Framework Integration: Configures RAGAS, G-Eval, DeepEval, and other evaluation frameworks dynamically
• Metric Evaluation: Measures performance using customizable evaluation metrics and scoring methods
• Feedback Integration: Supports human/manual feedback mechanisms for validation and improvement
• Structured Reporting: Generates detailed evaluation reports for performance analysis and optimization
• Extensible Architecture: Enables seamless addition of new LLM tasks and evaluation pipelines

Objectives

• Develop an evaluation pipeline for multiple LLM tasks such as RAG, summarization, and classification
• Support both online and offline evaluation workflows using ground-truth datasets
• Improve model reliability through standardized evaluation frameworks and metrics
• Enable flexible feedback integration for manual and automated assessment
• Generate structured performance reports for decision-making and optimization
• Build a scalable evaluation framework extendable to new AI use cases

Outcomes

• Reliable evaluation of LLM performance across diverse AI tasks
• Support for both online and offline testing environments
• Flexible integration of metrics, frameworks, and feedback methods
• Faster identification of model strengths and improvement areas
• Standardized reporting for informed AI optimization decisions
• Scalable framework adaptable to evolving enterprise AI requirements