Balancing AI and Rigor: How LLMs Are Reshaping Evaluation Practices at Global Scale

In a decisive yet measured move into the future of evaluation, the Independent Evaluation Group (IEG) of the World Bank and the Independent Office of Evaluation (IOE) of the International Fund for Agricultural Development (IFAD) have jointly released a landmark guidance note titled “Balancing Innovation and Rigor: Guidance for the Thoughtful Integration of Artificial Intelligence for Evaluation.” This document explores the transformative potential of large language models (LLMs), a subset of generative artificial intelligence, within the delicate, high-stakes world of global development evaluation. The note presents a roadmap to using LLMs like OpenAI’s GPT-4o in ways that improve efficiency and scope, while maintaining the stringent rigor required in evaluations that often influence policy and lives.

Breaking Down Complex Workflows into AI-Friendly Tasks

Rather than chasing the hype surrounding AI, the research teams took a methodical approach to testing LLMs in practice. Their primary use case was the structured literature review (SLR), a staple in major evaluations. Using a granular workflow designed through a data science lens, they identified specific moments where AI could add meaningful value. These moments included screening documents for relevance, extracting relevant content, annotating text, summarizing it within thematic types, and synthesizing it across categories.

This modular strategy enabled them to test components like classification and summarization separately. For instance, they found that LLMs could significantly reduce the burden of identifying thousands of potentially relevant papers in a short time, improving not just speed but coverage. This modularity also supported the reuse of successful components across other evaluation tasks, such as portfolio reviews and transcript analysis. Each task was mapped with appropriate evaluation metrics and checked against human judgment to ensure reliability.

Putting Generative AI to the Test: Metrics, Models, and Results

The experiments tested four main tasks using the GPT-4o and GPT-4o-mini models: text classification, summarization, synthesis, and information extraction. In classification, the model identified literature on private sector engagement in epidemic preparedness with an accuracy of 0.90, recall of 0.75, and a precision of 0.60, strong performance for a nuanced subject area. The summarization task scored impressively high, with a relevance score of 4.87 out of 5 and coherence of 4.97. Even more reassuring, no hallucinated content was detected, and factual alignment (faithfulness) stood at 0.90.

The synthesis task, which involved combining insights from multiple paper summaries, produced coherent and factually correct results, though with a slightly lower relevance score of 4.20 due to some omissions. For information extraction, pulling out details like actors and mechanisms from text, the models demonstrated perfect factual consistency but struggled with relevance, scoring only 3.25. Despite these minor gaps, the experiments proved that when guided well, LLMs can deliver trustworthy outputs even in evaluative contexts.

How Good Prompts and Smart Sampling Drive Results

At the heart of these successful applications was not just the AI model, but the human-crafted prompts. The team emphasized the critical importance of prompt design, beginning with a basic instruction set and iteratively refining it based on model performance. Prompts included a defined role for the model (e.g., “evaluation analyst”), stepwise task instructions, representative labeled examples, and even options for the model to admit uncertainty.

In addition to prompt engineering, sampling methodology played a pivotal role. Rather than relying on random samples, which risk overrepresenting dominant document types, the team used semantic clustering to create more diverse and representative subsets. Embedding vectors were computed for each paper, and clustering algorithms helped ensure that the prompts and model performance were tested across a wide range of document types and topics. This strategy helped mitigate biases and improve the generalizability of the findings across different use cases.

A Future of Collaborative, Responsible AI in Evaluation

This guidance note is more than just a technical manual, it’s a manifesto for responsible, collaborative innovation. The authors stress that AI is not a shortcut or replacement for human expertise. Rather, it is a tool that, when used judiciously, can elevate the quality and speed of evaluation work. They advocate for continued experimentation, shared learning, and open collaboration across institutions to build a robust knowledge base on what works, what doesn’t, and under what conditions.

The process, they argue, must be iterative and transparent. Thresholds for acceptable performance should be discussed among stakeholders and adapted to each specific task. Intercoder reliability, human validation, confusion matrices, and context-aware metric selection are all essential to preserving analytical rigor while leveraging the speed and scalability of LLMs.

Balancing Promise and Precision in the Age of AI

By documenting their journey, the IEG and IFAD have created a foundational resource for evaluators and policy analysts worldwide. They show that it is possible to embrace artificial intelligence without compromising professional standards or ethical boundaries. The message is clear: AI can support, but not substitute, the human responsibility of judgment, context interpretation, and critical analysis. In the hands of thoughtful practitioners, LLMs are not a threat to rigor but a potential ally in tackling ever-growing volumes of information. Through careful design, robust evaluation, and ongoing learning, these tools can help the development sector do more with clarity, speed, and accountability.