Self-Reflective RAG with Conformal Prediction for Reliable Question Answering in Low-Resource Domains

Abstract

Retrieval-augmented generation has become a central architecture for question answering systems that must combine parametric language model knowledge with external evidence. In high-resource settings, these systems can achieve strong performance, but their reliability remains fragile in low-resource domains where evidence is sparse, terminologies are specialized, and errors carry material consequences. This paper examines a system-level approach that integrates self-reflective retrieval-augmented generation with conformal prediction to support reliable question answering in low-resource domains. Self-reflection allows the system to critique its own retrieval and generation behavior, identify gaps in evidence, reformulate queries, and abstain when confidence is insufficient. Conformal prediction contributes distribution-free reliability signals by producing calibrated sets of plausible answers or decisions, allowing institutions to reason about coverage and risk without relying solely on model-reported confidence. The paper argues that the combination is not merely a model improvement but an infrastructure and governance strategy. It discusses architectural trade-offs involving retrieval indices, calibration data, latency, human oversight, and domain adaptation. It also considers deployment in clinical, legal, disaster response, agricultural, and Indigenous language contexts, where data scarcity intersects with fairness, sovereignty, and policy concerns. The analysis concludes that reliable question answering in low-resource domains requires socio-technical design: self-reflection improves adaptive behavior, conformal prediction provides auditable uncertainty, and governance mechanisms determine whether these capabilities translate into trustworthy public and professional use.