The ZeroHAL Framework: Targeting Zero Hallucination
Keywords:
Agentic AI, DiskANN, Hallucination mitigation, Hierarchical Navigable Small World (HNSW), Loop engineering, Parent-child chunking, Retrieval-augmented generationAbstract
Hallucination in large language model systems constitutes a documented and operationally significant reliability risk, with fabricated or contextually inconsistent outputs reported across healthcare, legal, and financial deployments. The article proposes a Zero Hallucination (ZeroHAL) framework comprising a unified thirteen-stage hierarchical framework for retrieval, evaluation, deployment, unified control, and enforcement for systematically diminishing hallucination in agentic retrieval augmented generation pipelines, encompassing thirteen functional stages: (i) clean ingestion, (ii) preprocessing, (iii) parent-child chunking, (iv) ontology-relative embedding, (v) indexing strategy, (vi) storage architecture, (vii) graph orchestration and loop engineering, (viii) retrieval modality selection, (ix) parameter tuning, (x) LLM configuration and context engineering, (xi) human oversight, (xii) evaluation metrics, and (xiii) observability and guardrails. The framework addresses a critical gap in the published literature: while individual mitigation strategies exist in isolation, no unified pipeline-level methodology has been proposed that integrates all of these concerns into a single reproducible workflow. The thirteen stages are instantiated for major cloud platforms including Amazon Web Services, Microsoft Azure, Google Cloud Platform, Databricks, Snowflake, and Palantir Foundry, and Python-native libraries including LangGraph and the SAMF framework (which is based on the MoSCoW – must-have, should-have, could-have, and won’t-have method). An enterprise use case demonstrates application to a 100-million-item heterogeneous corpus spanning seven modality types like documents, spreadsheets, visual content, plain text, tabular data, video, and audio, with index configuration trade-off analysis showing that hallucination-free response generation is achievable across all configurations through faithfulness gating and grounded non-answer protocols.
References
Z. Ji et al. “Survey of hallucination in natural language generation,” ACM Computing Surveys, vol. 55, no. 12, pp. 1-38, Mar. 2023.
Y. Zhang et. al., “Siren's song in the AI ocean: A survey on hallucination in large language models,” arXiv:2309.01219, 2023.
P. Lewis et al. “Retrieval augmented generation for knowledge-intensive NLP tasks,” NIPS'20: Proceedings of the 34th International Conference on Neural Information Processing Systems, pp. 9459–9474, 2020.
G. Izacard and E. Grave, “Leveraging passage retrieval with generative models for open domain question answering,” in Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics, pp. 874–880, Apr. 2021.
P. D. Sawant, “Automation-Multi-AI (AMAI): An integrated multi-AI architecture for CPU-based analysis of complex structured workflows,” Journal of Advances in Artificial Intelligence, vol. 3, no. 2, 154–168, Jun. 2025.
N. Shinn, F. Cassano, E. Berman, A. Gopinath, K. Narasimhan, and S. Yao, “Reflexion: Language agents with verbal reinforcement learning,” NIPS '23: Proceedings of the 37th International Conference on Neural Information Processing Systems, pp. 8634-8652, 2023.
P. D. Sawant, “Unified, modular, self-learning, real-time agentic AI ecosystem: Integrating multi-modal data, automation, feedback loops, and secure agent orchestration,” Recent Trends in Artificial Intelligence & Its Applications, vol. 4, vo. 2, pp. 43–48, 2025.
P. D. Sawant, “SAMF: SAWANT (Structured Agentic Workflow for Alignment, Validation, and Negotiated Testing) for reliable, safe, and verifiable LLM prompting,” Preprints.org, Apr. 2026.
P. Sawant, “A real-time visualization framework to enhance prompt accuracy and result outcomes based on number of tokens,” Journal of Artificial Intelligence Research & Advances, vol. 11, no. 1, pp. 45-53, 2024.
L. Huang et. al., “A survey on hallucination in large language models: Principles, taxonomy, challenges, and open questions,” ACM Transactions on Information Systems, vol. 43, no. 2, pp. 1–55, 2025.
X. Yang et al., “CRAG—Comprehensive RAG benchmark,” in Advances in Neural Information Processing Systems 37 (NeurIPS 2024), pp. 10470–10490, 2024.
A. Balaguer et al., “RAG vs fine-tuning: Pipelines, tradeoffs, and a case study on agriculture,” arXiv preprint arXiv:2401.08406, 2024.
D. Edge et. al. “From local to global: A graph RAG approach to query-focused summarization,” arXiv:2404.16130, 2024.
P. Sarthi, S. Abdullah, A. Tuli, S. Khanna, A. Goldie, and C. D. Manning, “RAPTOR: Recursive abstractive processing for tree-organized retrieval,” in Proceedings 12th International Conference on Learning Representations (ICLR), 2024.
P. D. Sawant, “Agentic AI: A quantitative analysis of performance and applications,” Journal of Advanced AI, vol. 3, no. 2, pp. 132–140, 2025.
R. Rafailov, A. Sharma, E. Mitchell, C. D. Manning, S. Ermon, and C. Finn, “Direct preference optimization: Your language model is secretly a reward model,” in Advances in Neural Information Processing Systems 36 (NeurIPS 2023), pp. 53728–53741, 2023.
L. Ouyang et al., “Training language models to follow instructions with human feedback,” in Advances in Neural Information Processing Systems 35 (NeurIPS 2022), pp. 27730–27744, 2022.
S. Dhuliawala et. al., “Chain-of-verification reduces hallucination in large language models,” in Findings of the Association for Computational Linguistics: ACL 2024, Bangkok, Thailand, pp. 3563–3578, 2024.
Y.-S. Chuang, Y. Xie, H. Luo, Y. Kim, J. R. Glass, and P. He, “DoLa: Decoding by contrasting layers improves factuality in large language models,” in Proceedings International Conference on Learning Representations (ICLR), 2024.
L. Kuhn, Y. Gal, and S. Farquhar, “Semantic uncertainty: Linguistic invariances for uncertainty estimation in natural language generation,” in Proceedings 11th International Conference on Learning Representations (ICLR), 2023.
Claude Platform Docs “Prompting Best Practices,” Claude API Docs. Accessed: Oct. 03, 2026.
Y. Zhou, A. I. Muresanu, Z. Han, K. Paster, S. Pitis, H. Chan, and J. Ba, “Large language models are human-level prompt engineers,” in Proceedings 11th International Conference on Learning Representations (ICLR), 2023.
T. Brown et al., “Language models are few-shot learners,” in Advances in Neural Information Processing Systems 33 (NeurIPS 2020), vol. 33, pp. 1877–1901, 2020.
S. Robertson and H. Zaragoza, “The probabilistic relevance framework: BM25 and beyond,” Foundations and Trends in Information Retrieval, vol. 3, no. 4–5, pp. 333–389, 2009.
Y. A. Malkov and D. A. Yashunin, "Efficient and Robust Approximate Nearest Neighbor Search Using Hierarchical Navigable Small World Graphs," in IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 42, no. 4, pp. 824-836, April 2020.
N. Reimers and I. Gurevych, “Sentence-BERT: Sentence embeddings using Siamese BERT-networks,” Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), Hong Kong, China, 2019, pp. 3982–3992.
N. F. Noy and D. L. McGuinness, “Ontology development 101: A guide to creating your first ontology,” Stanford University, Stanford, CA, USA, Stanford Knowledge Systems Laboratory Tech. Rep. KSL-01-05, 2001.
S. J. Subramanya, D. Devvrit, H. V. Simhadri, R. Krishnawamy, and R. Kadekodi, “DiskANN: Fast accurate billion-point nearest neighbor search on a single node,” in Advances in Neural Information Processing Systems (NeurIPS 2019), vol. 32, 2019.
A. Vaswani et. al., “Attention is all you need,” in Advances in Neural Information Processing Systems 30 (NeurIPS 2017), Long Beach, CA, USA, pp. 5998–6008, 2017.
F. Perez and I. Ribeiro, “Ignore previous prompt: Attack techniques for language models,” arXiv preprint arXiv:2211.09527, 2022.
S. Es, J. James, L. Espinosa-Anke, and S. Schockaert, “RAGAS: Automated evaluation of retrieval augmented generation,” Proceedings of the 18th Conference of the European Chapter of the Association for Computational Linguistics: System Demonstrations, pp. 150-158, Mar. 2024.
X. Wang et. al., “Self-consistency improves chain of thought reasoning in language models,” arXiv:2203.11171, 2023.
A. d’Avila Garcez and L. C. Lamb, “Neurosymbolic AI: The 3rd wave,” Artificial Intelligence Review, vol. 56, no. 11, pp. 12387–12406, 2023.
