LLMs · EVIDENCE SYNTHESIS · CORPORATE EVALUATION

AI Chatbot for Interview-Based Evidence Retrieval: Using Local LLMs in Corporate Evaluation

Q: What institutional support was needed to make this work?

Success required an IOE AI Strategy, collaboration with ICT division, knowledge sharing with sister agencies, and an internal Community of Practice on AI and GIS.

Hannah Den Boer, Associate Evaluation Officer at IFAD's Independent Office of Evaluation, led the use of a local Large Language Model (LLM) chatbot to query over 90 interview transcripts as part of a corporate-level evaluation. The tool significantly sped up the analysis phase while maintaining rigor through careful validation methods—including requiring verbatim quotes with timestamps, human scoring of relevance and accuracy, and proactive mitigation of confirmation bias and hallucinations. The case demonstrates how AI can enhance efficiency in evaluation when implemented with thoughtful safeguards and institutional support.

Focus: Corporate-level evaluation & evidence synthesis AI Methods: Local LLMs, chatbot interface, RAG (Retrieval-Augmented Generation) Context: IFAD Corporate-level Evaluation (CLE IFAD11-12) Data: 90+ interview transcripts with HQ staff

PRIMARY SOURCE IFAD Independent Office of Evaluation – Official Case Study

This case study is based on the official IFAD IOE documentation:

AI chatbot supports interview-based evidence retrieval – IFAD IOE

The following write-up integrates insights from this official source along with related coverage from WFP Evaluation and presentation materials.

PRIMARY SOURCE: IFAD IOE Case Study Read: WFP Evaluation – Four Examples of AI in Evaluation Download presentation slides

What is this case study about?

This case study documents the innovative use of a local Large Language Model (LLM) chatbot at the Independent Office of Evaluation (IOE) of IFAD (International Fund for Agricultural Development). Led by Hannah Den Boer, Associate Evaluation Officer, the project aimed to enhance the efficiency of evidence retrieval during the Corporate-level Evaluation (CLE) of IFAD11 and IFAD12 (2019-2024)—a comprehensive assessment of institutional and operational performance.

The evaluation drew on multiple evidence streams, including synthesis from recent evaluations, document and data review, country case studies, portfolio analysis, thematic deep dives, e-survey, evaluation of impact assessment methodology, and over 90 interview transcripts with HQ staff. The team deployed a chatbot interface powered by local LLMs to query this large corpus of interview data, significantly accelerating the analysis phase while maintaining rigorous validation protocols.

90+

Interview transcripts

HQ Staff

Corporate-level evaluation

Local LLMs

Data privacy protected

Verbatim + timestamps

Validation protocol

Why was this approach necessary?

Corporate-level evaluations typically involve synthesizing evidence from multiple streams, including dozens or even hundreds of interview transcripts. Traditional approaches to analyzing such qualitative data are labor-intensive and time-consuming. Evaluators face several challenges:

Volume: Manually reviewing over 90 one-hour transcripts is a significant investment of evaluator time.
Complexity: Identifying cross-cutting themes and patterns across multiple interviews requires systematic analysis.
Iteration: As analysis progresses, evaluators often need to return to transcripts with new questions—a process that is cumbersome with manual methods.
Collaboration: Multiple team members need to access and query the same evidence base efficiently.

The Innovation: Deploying a local LLM chatbot to enable rapid, iterative querying of interview data while maintaining transparency, traceability, and evaluator control through rigorous validation methods.

Context: The Corporate-Level Evaluation (CLE IFAD11-12)

The CLE of IFAD11 and IFAD12 was a comprehensive evaluation covering IFAD's institutional and operational performance from 2019 to 2024. The evidence base included:

Synthesis from recent evaluations

Building on existing evaluation findings to establish baseline understanding.

Document and data review

Analysis of institutional documents, reports, and performance data.

Country case studies

In-depth examinations of IFAD operations in selected countries.

Portfolio analysis

Quantitative analysis of IFAD's project portfolio.

Thematic deep dives

Focused analysis on cross-cutting themes like climate, gender, and nutrition.

E-survey

Stakeholder surveys to gather perceptions and feedback.

Impact Assessment methodology evaluation

Assessment of IFAD's approach to measuring impact.

HQ interviews

Over 90 interviews with IFAD headquarters staff—the focus of the AI chatbot application.

How did the AI chatbot work?

The team implemented a chatbot interface designed specifically for interview-based evidence retrieval, with careful attention to data governance and validation.

Technical approach

Local LLM deployment: The model ran locally, ensuring compliance with IFAD's data governance policy and protecting sensitive interview data.
Chatbot interface: Evaluators could ask natural language questions about the interview corpus and receive structured responses.
Retrieval-Augmented Generation (RAG): The system retrieved relevant transcript segments before generating responses, ensuring grounding in source material.

Validation method: Ensuring rigor

To maintain evaluation standards, the team implemented a three-part validation protocol for all AI-generated outputs:

Verbatim quotes
The system was prompted to return direct quotes from transcripts, not paraphrased summaries.

Time stamps
Each quote was accompanied by its timestamp in the original interview, enabling precise verification.

Quick links
Direct links to the original transcript allowed evaluators to instantly access source material.

Scoring
Evaluators scored AI outputs on relevance, accuracy, and completeness to track performance.

What were the gains?

The team identified several significant benefits from integrating the AI chatbot into the evaluation workflow:

Enhanced (iterative) efficiency

Evaluators could rapidly query the interview corpus with new questions as analysis progressed, eliminating the need for manual re-reading of transcripts.

Structural transparency

The chatbot's responses were traceable to specific transcript segments, making the evidence base for findings explicit and auditable.

Co-occurrence patterns

The AI helped identify patterns and connections across interviews that might have been missed in manual analysis, revealing relationships between themes and topics.

Collaborative advantage

Multiple team members could query the same evidence base consistently, supporting shared understanding and collaborative analysis.

What were the challenges and how were they mitigated?

The team was transparent about the risks and limitations of using LLMs in evaluation, implementing deliberate mitigation strategies:

1. Confirmation bias

Risk: LLMs may selectively retrieve information that confirms pre-existing beliefs or hypotheses, leading to biased findings.

Mitigation: Evaluators constantly engaged with interactive prompts, explicitly requesting counterevidence to claims made. This forced the system to surface dissenting views and contradictory evidence.

2. Hallucinations and incorrect responses

Risk: LLMs can generate plausible-sounding but incorrect information (hallucinations), undermining credibility.

Mitigation: The system was prompted to return verbatim quotes with timestamps and links to original transcripts, enabling evaluators to manually verify every entry. This transformed the AI from a "black box" into a tool for efficient retrieval of verifiable evidence.

3. Loss of contextual weighting

Risk: AI systems may implicitly treat all sources equally, failing to reflect the evaluator's understanding of which interviews or respondents carry more weight or expertise.

Mitigation: Evaluators factored this limitation into their interpretation of findings, applying their contextual knowledge to appropriately weight sources.

4. Overreliance on extracts

Risk: Focusing on AI-generated extracts can lead to losing the holistic, embodied familiarity with the data that comes from manual analysis.

Mitigation: The team was mindful of this risk and ensured that AI was used as a complement to, not a replacement for, deep engagement with the evidence base.

Enabling environment: Institutional support at IOE

The successful implementation of this AI tool was supported by a robust institutional enabling environment at IFAD's Independent Office of Evaluation:

IOE AI Strategy: A clear institutional strategy provided the framework for responsible AI adoption in evaluation.
Collaboration with IFAD's ICT division: Technical support from ICT ensured secure deployment and compliance with data governance requirements.
Collaboration with sister agencies: Knowledge sharing with organizations like the World Bank Group's Independent Evaluation Group (IEG) supported learning and best practice development.
IOE Community of Practice on AI and GIS: An internal community of practice provided peer learning, troubleshooting, and methodological guidance.

Key lessons for evaluators and M&E practitioners

LLMs can accelerate qualitative analysis

With careful implementation, AI can significantly speed up evidence retrieval and synthesis without sacrificing rigor—enabling evaluators to spend more time on interpretation and judgment.

Local deployment protects sensitive data

Running LLMs locally rather than in the cloud ensures compliance with data governance policies and protects confidential interview data.

Verbatim quotes + timestamps = trust

Requiring the system to return direct quotes with timestamps and links transforms AI from a black box into a traceable, verifiable tool.

Actively mitigate confirmation bias

Prompting the system to explicitly request counterevidence helps ensure balanced analysis and prevents selective retrieval of confirming information.

AI outputs require human weighting

Evaluators must apply contextual knowledge to weight AI-generated extracts appropriately—AI cannot replace professional judgment on source credibility.

Institutional support is essential

AI adoption in evaluation requires more than technical tools—it demands strategy, governance, cross-functional collaboration, and communities of practice.

Frequently asked questions

How did the team ensure the AI didn't hallucinate or produce incorrect information?

The system was specifically prompted to return verbatim quotes with timestamps and links to the original transcripts, rather than paraphrased summaries. This allowed evaluators to manually verify every AI-generated entry against the source material, transforming the tool from a "black box" into a traceable, verifiable evidence retrieval system.

How did the team address the risk of confirmation bias?

Evaluators constantly engaged with the chatbot using interactive prompts that explicitly requested counterevidence to claims made. This forced the system to surface dissenting views and contradictory evidence, ensuring that analysis considered multiple perspectives rather than selectively retrieving confirming information.

Why was it important to run the LLM locally rather than in the cloud?

Running the LLM locally ensured compliance with IFAD's data governance policy and protected sensitive interview data. Sending interview transcripts to cloud-based AI services would have raised significant privacy and security concerns, particularly for a corporate-level evaluation involving confidential staff perspectives.

What institutional support was needed to make this work?

Success required multiple institutional elements: an IOE AI Strategy providing the framework for responsible adoption; collaboration with IFAD's ICT division for technical deployment; knowledge sharing with sister agencies like the World Bank's IEG; and an internal Community of Practice on AI and GIS to support learning and troubleshooting.

What are the main takeaways for other evaluation offices?

Key takeaways include: (1) LLMs can significantly accelerate qualitative analysis while maintaining rigor when paired with proper validation; (2) local deployment is essential for data privacy; (3) requiring verbatim quotes with timestamps enables verification and builds trust; (4) confirmation bias must be actively mitigated; and (5) institutional strategy, ICT collaboration, and communities of practice are critical enablers.

Main reference & original sources

This case study is based on the official documentation from IFAD's Independent Office of Evaluation and related presentations:

1. PRIMARY SOURCE – IFAD IOE (2025/2026). AI chatbot supports interview-based evidence retrieval. Independent Office of Evaluation of IFAD. https://ioe.ifad.org/en/w/ai-chatbot-supports-interview-based-evidence-retrieval
2. WFP Evaluation (2026, March 23). Four examples of how AI, machine learning and data innovation are reshaping evaluation and research. Medium. https://wfp-evaluation.medium.com/four-examples-of-how-ai-machine-learning-and-data-innovation-are-reshaping-evaluation-and-research-f4d1b3cf4e74
3. Den Boer, H. (2025, December). AI Chatbot for Interview-Based Evidence Retrieval. Presentation slides. WFP Global Impact Evaluation Forum 2025. Download full presentation (PDF)

Interpretation and analysis by EvalCommunity: The insights, lessons, and expert commentary presented in this article are offered by EvalCommunity as an independent interpretation of the documented study for the M&E community. They are based on the official IFAD IOE case study, the WFP Evaluation Medium article, and the presentation slides by Hannah Den Boer. EvalCommunity does not claim ownership of the original research or findings. We encourage readers to consult the original sources for the complete context.

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