Abstract

Large Language Models (LLMs) have demonstrated remarkable capabilities in natural language understanding and generation, however, their practical adoption remains constrained by a critical limitation: the tendency to produce fluent yet unverified or fabricated content. In enterprise and knowledge-intensive environments, such hallucinations significantly undermine reliability and user trust.

CiteMind is a production-oriented Retrieval-Augmented Generation (RAG) system designed to address this challenge through citation grounding and semantic validation. The system retrieves context from a large-scale document corpus and augments generation with source-linked evidence. A post-generation semantic similarity framework quantifies alignment between the generated response and retrieved material, enabling hallucination-aware scoring.

CiteMind was evaluated on a corpus exceeding 3 GB of research and financial documents using over 1,200 semantic queries reflecting realistic usage scenarios. The system achieved a median end-to-end latency of under one second in a containerized deployment. Compared to a baseline RAG configuration, the proposed citation-grounded validation framework improved grounded response accuracy by 22%, demonstrating enhanced factual consistency and more reliable, evidence-aligned generation for real-world applications.

Keywords:
Large Language Models, Retrieval-Augmented Generation, Citation Grounding, Hallucination Detection, Semantic Retrieval

Introduction

Generative AI systems are increasingly being integrated into enterprise workflows, research platforms, and decision-support tools. Despite their linguistic fluency, contemporary LLMs frequently generate information that is plausible but unsupported by factual evidence. This phenomenon, commonly referred to as hallucination, poses significant risks in domains where accuracy and traceability are essential.

Retrieval-Augmented Generation (RAG) has emerged as a promising approach to mitigate hallucination by providing models with relevant contextual information at inference time. However, many existing implementations assume that retrieved context alone guarantees factual consistency. In practice, models may still introduce unsupported claims, omit critical evidence, or misinterpret retrieved material.

CiteMind was developed to address this gap by introducing a system that prioritizes not only retrieval and generation, but also verification. The objective is to enable users to receive responses that are transparent, evidence-linked, and accompanied by measurable confidence in their grounding. The project reflects a broader shift within industry from conversational novelty towards trustworthy and accountable generative systems.

Methodology

CiteMind is designed as a modular, production-ready architecture comprising four primary components: data processing, semantic retrieval, grounded generation, and hallucination scoring.

1. Data Processing and Indexing

A heterogeneous corpus exceeding 3 GB of research and financial documents is processed through a structured ingestion workflow. Content is standardized and transformed into vector representations to support efficient large-scale semantic search.

2. Semantic Retrieval

At query time, user input is transformed into an embedding and matched against the vector index. The system retrieves the top-k context segments based on cosine similarity, ensuring high semantic relevance across a large-scale knowledge base.

3. Citation-Grounded Generation

Retrieved evidence is provided to the language model as contextual support during response generation. Outputs are returned with associated source references to enable transparency and user verification.

4. Hallucination Scoring

To assess response reliability, CiteMind incorporates a semantic validation layer that evaluates the degree of alignment between generated content and supporting evidence. This produces a grounding score that enables hallucination-aware assessment and improves overall factual consistency.

5. System Deployment

CiteMind is developed as a containerized, full-stack system designed to support scalable deployment and reproducible evaluation. The application exposes a lightweight interface for interactive querying while integrating a backend pipeline for retrieval-augmented generation and evidence alignment.

Semantic retrieval is performed over a large-scale vector index, and the generation workflow is managed through a modular orchestration layer. The system is containerized to ensure portability across environments and consistent performance under realistic usage conditions. This architecture enables reliable experimentation while supporting practical deployment scenarios without compromising efficiency or scalability.

Experiments

The system was evaluated under realistic usage conditions using a diverse set of semantic queries spanning research and financial domains. The evaluation was designed to assess two key aspects of system performance.

1. Grounded Response Accuracy

Responses were evaluated based on their semantic alignment with retrieved source material. A hallucination scoring framework was employed, measuring sentence-level similarity between generated content and supporting evidence to quantify grounding fidelity.

2. System Performance

Operational performance was assessed through latency and throughput measurements to determine the system’s suitability for interactive applications.

The evaluation was conducted on a document corpus exceeding 3 GB using over 1,200 query interactions designed to reflect realistic usage scenarios. Performance was assessed under a relevance-based retrieval framework with alignment-driven evaluation to measure the degree of evidence grounding in generated responses. Baseline results were obtained using a standard retrieval-augmented generation configuration without the proposed validation mechanism, enabling a controlled comparison of improvements in grounding quality and system reliability.

Results

The experimental evaluation demonstrated measurable improvements in both reliability and operational efficiency.

• 22% improvement in grounded response accuracy compared with baseline RAG.

• Median (p50) latency below one second, supporting real-time interaction.

• Consistent source attribution accompanying generated responses.

The hallucination scoring mechanism proved effective in identifying unsupported content and improving evidence alignment. Notably, the system maintained low latency despite operating over a large-scale corpus, indicating its suitability for deployment in practical environments.

Conclusion

CiteMind illustrates that reliable deployment of generative AI requires more than retrieval augmentation alone. By integrating citation grounding with semantic validation, the system enhances factual consistency and transforms generative outputs into verifiable, evidence-backed responses.

The work emphasises several principles essential for enterprise adoption of generative systems:

• Transparency, through explicit source attribution

• Reliability, enabled by alignment-based validation

• Scalability, supported by a production-oriented architecture

• Practical usability, achieved through low-latency performance

CiteMind represents a step towards trustworthy generative systems capable of supporting research workflows, enterprise knowledge management, and decision-support applications. More broadly, the project reflects the growing industry emphasis on measurable reliability, interpretability, and accountability as foundational requirements for real-world AI deployment.

#Code and Demo

Live Demo

An interactive deployment of CiteMind is available via Hugging Face Spaces. The demo allows users to submit semantic queries and observe the complete workflow, including evidence retrieval, citation-grounded response generation, and alignment-aware output.

Demo

Source Code

The full implementation is available as an open-source repository. The project provides a production-oriented Retrieval-Augmented Generation pipeline with modular components for semantic retrieval, grounded generation, and reliability evaluation.

Repository

The codebase demonstrates:

• End-to-end RAG system design

• Integration of citation-grounded generation

• Alignment-based hallucination assessment

• Containerized deployment for reproducibility

• Configuration details and environment setup instructions are provided within the repository.

Data Availability

The evaluation corpus (3+ GB) consists of publicly available research and financial documents curated to simulate enterprise knowledge environments. The dataset is privately maintained and is not distributed as a public download.

References

Abdi et al., HalluRAG-RUG, SemEval 2025.

Niu et al., RAGTruth: A Hallucination Corpus for Trustworthy RAG, ACL 2024.

Ding et al., Rowen: Adaptive RAG for Hallucination Mitigation, arXiv 2024.

Gao et al., Retrieval-Augmented Generation for Large Language Models: A Survey, 2023.

Lewis et al., Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks, NeurIPS 2020.

Karpukhin et al., Dense Passage Retrieval for Open-Domain QA, EMNLP 2020.