RAG pipeline for private well contamination guidance: A comparative study of retrieval and generation strategies
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Access to safe drinking water remains a fundamental public health priority, particularly in rural and semi-urban areas where private wells are a primary source but often lack proper monitoring. This exposes users to microbiological risks such as E.coli and coliform bacteria. Although large language models (LLMs) hold promise in delivering accessible guidance, their performance in specialized low-resource domains remains limited. In this study, we develop a domain-adapted Retrieval-Augmented Generation (RAG) system tailored to support private well owners with contamination concerns. Starting from a naive RAG baseline, we explore key enhancements, including embedding model fine-tuning (BGE-M3) using synthetic QA pairs, query rewriting, and an adaptive reranking technique. Evaluation combines LLM-as-judge metrics via the deepeval framework, statistical significance testing, and expert review of the generated answers. Adaptive reranking with Llama delivered the highest performance (86.34% answer relevancy, 91.6% faithfulness), improved contextual relevancy, and received the highest expert-rated technical accuracy, demonstrating its advantage in factual correctness.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it