Research Article
Evaluating Precision and Recall at Retrieval Time in Retrieval-Augmented Generation (RAG) Systems
Gopichand Agnihotram*
,
Joydeep Sarkar
Issue:
Volume 8, Issue 4, December 2025
Pages:
174-180
Received:
12 September 2025
Accepted:
23 September 2025
Published:
18 October 2025
DOI:
10.11648/j.ajcst.20250804.11
Downloads:
Views:
Abstract: Retrieval-Augmented Generation (RAG) systems signify a pivotal advancement in natural language processing, merging information retrieval with large language models (LLMs) to ground responses in external knowledge. This hybrid approach enhances the factual accuracy and currency of generated content, mitigating common issues like hallucination. The efficacy of a RAG system, however, is fundamentally dependent on the performance of its retrieval component. This paper provides a detailed analysis of precision and recall as critical metrics for evaluating and optimizing this retrieval step. We explore the distinct roles and inherent trade-offs of these metrics within a RAG pipeline, demonstrating their direct influence on the quality of the final output. Through a series of experiments comparing sparse (BM25), dense (DPR), and hybrid retrieval methods, we quantify their performance characteristics. The analysis is further enriched with real-world examples from finance, law, and healthcare, illustrating the practical implications of retrieval quality. Additionally, we outline advanced strategies for improving retrieval effectiveness, such as multi-stage architecture involving rerankers and the use of query transformations. The paper concludes with a set of best practices for deploying robust, enterprise-grade RAG systems, emphasizing the need for continuous evaluation and sophisticated retrieval strategies. By focusing on the systematic optimization of precision and recall, organizations can build more reliable and trustworthy AI applications.
Abstract: Retrieval-Augmented Generation (RAG) systems signify a pivotal advancement in natural language processing, merging information retrieval with large language models (LLMs) to ground responses in external knowledge. This hybrid approach enhances the factual accuracy and currency of generated content, mitigating common issues like hallucination. The e...
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Research Article
Beyond Static Retrieval: A Reinforcement Learning Framework for Dynamic and Adaptive RAG
Gopichand Agnihotram,
Joydeep Sarkar,
Magesh Kasthuri*
Issue:
Volume 8, Issue 4, December 2025
Pages:
181-188
Received:
12 September 2025
Accepted:
23 September 2025
Published:
18 October 2025
DOI:
10.11648/j.ajcst.20250804.12
Downloads:
Views:
Abstract: Retrieval-Augmented Generation (RAG) is a widely adopted technique that enhances large language models (LLMs) by grounding their outputs in external knowledge sources. This approach reduces hallucinations, increases factual accuracy, and adapts well to rapidly evolving domains. Despite these strengths, traditional RAG implementations rely on static, heuristic-based retrieval strategies that operate independently of feedback or contextual learning. In today’s fast-changing information landscape, it’s crucial for language models to go beyond static retrieval when grounding their responses. That’s where a RL framework comes into play for RAG. Rather than sticking to fixed, rule-based selection methods, RL allows the retrieval component to learn and adapt over time—much like how a person refines their search strategies with experience and feedback. By framing the process of document selection as a Markov Decision Process (MDP), the system can make context-aware choices that consider both immediate and future gains. This white paper explores how Retrieval-Augmented Generation can be significantly enhanced by integrating Markov Decision Processes (MDPs) and Reinforcement Learning (RL). We present a conceptual framework that models retrieval as a sequential decision-making problem. By treating document selection as an MDP and employing RL algorithms to optimize retrieval strategies, we introduce adaptivity, context sensitivity, and long-term reasoning into the RAG pipeline, leading to demonstrably more accurate and relevant generated content. The paper also outlines applications, implementation strategies, and future research directions that combine symbolic and neural methods for improved decision-making and document relevance.
Abstract: Retrieval-Augmented Generation (RAG) is a widely adopted technique that enhances large language models (LLMs) by grounding their outputs in external knowledge sources. This approach reduces hallucinations, increases factual accuracy, and adapts well to rapidly evolving domains. Despite these strengths, traditional RAG implementations rely on static...
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