# Seven Failure Points When Engineering a Retrieval Augmented Generation System

arXiv: https://arxiv.org/abs/2401.05856 · HTML: https://mlsanity.com/paper/2401.05856/

## Key facts

- Cited by 139 papers in the map (data through 14 Oct 2026).
- Placed in RAG pipeline design and evaluation, one of 15 directions in Retrieval-augmented generation.
- Builds on 10 of its 21 references that are in the map.
- First posted on arXiv 11 Jan 2024.

## Placed in

- Retrieval-augmented generation / [RAG pipeline design and evaluation](https://mlsanity.com/rag/rag-pipeline-design-and-evaluation/)

## Builds on (most cited first, 10 of 10)

- [Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks](https://mlsanity.com/paper/2005.11401/) (2020)
- [Robust Speech Recognition via Large-Scale Weak Supervision](https://mlsanity.com/paper/2212.04356/) (2022)
- [Lost in the Middle: How Language Models Use Long Contexts](https://mlsanity.com/paper/2307.03172/) (2023)
- [Leveraging Passage Retrieval with Generative Models for Open Domain Question Answering](https://mlsanity.com/paper/2007.01282/) (2020)
- [Large Language Models for Information Retrieval: A Survey](https://mlsanity.com/paper/2308.07107/) (2023)
- [Benchmarking Large Language Models in Retrieval-Augmented Generation](https://mlsanity.com/paper/2309.01431/) (2023)
- [Improving the Domain Adaptation of Retrieval Augmented Generation (RAG) Models for Open Domain Question Answering](https://mlsanity.com/paper/2210.02627/) (2022)
- [FiD-Light: Efficient and Effective Retrieval-Augmented Text Generation](https://mlsanity.com/paper/2209.14290/) (2022)
- [Beware the evolving 'intelligent' web service! An integration architecture tactic to guard AI-first components](https://mlsanity.com/paper/2005.13186/) (2020)
- [Threshy: Supporting Safe Usage of Intelligent Web Services](https://mlsanity.com/paper/2008.08252/) (2020)

## Built on by (most cited first, 30 of 139)

- [Retrieval-Augmented Generation for AI-Generated Content: A Survey](https://mlsanity.com/paper/2402.19473/) (2024)
- [Hallucination-Free? Assessing the Reliability of Leading AI Legal Research Tools](https://mlsanity.com/paper/2405.20362/) (2024)
- [Evaluation of Retrieval-Augmented Generation: A Survey](https://mlsanity.com/paper/2405.07437/) (2024)
- [Can Long-Context Language Models Subsume Retrieval, RAG, SQL, and More?](https://mlsanity.com/paper/2406.13121/) (2024)
- [Knowledge Graphs Meet Multi-Modal Learning: A Comprehensive Survey](https://mlsanity.com/paper/2402.05391/) (2024)
- [LLM-based Agents Suffer from Hallucinations: A Survey of Taxonomy, Methods, and Directions](https://mlsanity.com/paper/2509.18970/) (2025)
- [Financial Report Chunking for Effective Retrieval Augmented Generation](https://mlsanity.com/paper/2402.05131/) (2024)
- [TC-RAG:Turing-Complete RAG's Case study on Medical LLM Systems](https://mlsanity.com/paper/2408.09199/) (2024)
- [Retrieval-Augmented Generation: A Comprehensive Survey of Architectures, Enhancements, and Robustness Frontiers](https://mlsanity.com/paper/2506.00054/) (2025)
- [A Survey on Long-Term Memory Security in LLM Agents: Attacks, Defenses, and Governance Across the Memory Lifecycle](https://mlsanity.com/paper/2604.16548/) (2026)
- [T-RAG: Lessons from the LLM Trenches](https://mlsanity.com/paper/2402.07483/) (2024)
- [ConfusedPilot: Confused Deputy Risks in RAG-based LLMs](https://mlsanity.com/paper/2408.04870/) (2024)
- [HyPA-RAG: A Hybrid Parameter Adaptive Retrieval-Augmented Generation System for AI Legal and Policy Applications](https://mlsanity.com/paper/2409.09046/) (2024)
- [When to Retrieve: Teaching LLMs to Utilize Information Retrieval Effectively](https://mlsanity.com/paper/2404.19705/) (2024)
- [FinSage: A Multi-aspect RAG System for Financial Filings Question Answering](https://mlsanity.com/paper/2504.14493/) (2025)
- [LRP4RAG: Detecting Hallucinations in Retrieval-Augmented Generation via Layer-wise Relevance Propagation](https://mlsanity.com/paper/2408.15533/) (2024)
- [Are LLMs Correctly Integrated into Software Systems?](https://mlsanity.com/paper/2407.05138/) (2024)
- [FACTS About Building Retrieval Augmented Generation-based Chatbots](https://mlsanity.com/paper/2407.07858/) (2024)
- [AdapterSwap: Continuous Training of LLMs with Data Removal and Access-Control Guarantees](https://mlsanity.com/paper/2404.08417/) (2024)
- [Towards Reliable Retrieval in RAG Systems for Large Legal Datasets](https://mlsanity.com/paper/2510.06999/) (2025)
- [Beyond Similarity: Trustworthy Memory Search for Personal AI Agents](https://mlsanity.com/paper/2606.06054/) (2026)
- [Dialectical Alignment: Resolving the Tension of 3H and Security Threats of LLMs](https://mlsanity.com/paper/2404.00486/) (2024)
- [CommunityKG-RAG: Leveraging Community Structures in Knowledge Graphs for Advanced Retrieval-Augmented Generation in Fact-Checking](https://mlsanity.com/paper/2408.08535/) (2024)
- [Visual RAG: Expanding MLLM visual knowledge without fine-tuning](https://mlsanity.com/paper/2501.10834/) (2025)
- [Classifying and Addressing the Diversity of Errors in Retrieval-Augmented Generation Systems](https://mlsanity.com/paper/2510.13975/) (2025)
- [Generating Test Scenarios from NL Requirements using Retrieval-Augmented LLMs: An Industrial Study](https://mlsanity.com/paper/2404.12772/) (2024)
- [RE-Adapt: Reverse Engineered Adaptation of Large Language Models](https://mlsanity.com/paper/2405.15007/) (2024)
- [Towards Practical GraphRAG: Efficient Knowledge Graph Construction and Hybrid Retrieval at Scale](https://mlsanity.com/paper/2507.03226/) (2025)
- [InfMem: Learning System-2 Memory Control for Long-Context Agent](https://mlsanity.com/paper/2602.02704/) (2026)
- [Incorporating External Knowledge and Goal Guidance for LLM-based Conversational Recommender Systems](https://mlsanity.com/paper/2405.01868/) (2024)

## Abstract (arXiv)

Software engineers are increasingly adding semantic search capabilities to applications using a strategy known as Retrieval Augmented Generation (RAG). A RAG system involves finding documents that semantically match a query and then passing the documents to a large language model (LLM) such as ChatGPT to extract the right answer using an LLM. RAG systems aim to: a) reduce the problem of hallucinated responses from LLMs, b) link sources/references to generated responses, and c) remove the need for annotating documents with meta-data. However, RAG systems suffer from limitations inherent to information retrieval systems and from reliance on LLMs. In this paper, we present an experience report on the failure points of RAG systems from three case studies from separate domains: research, education, and biomedical. We share the lessons learned and present 7 failure points to consider when designing a RAG system. The two key takeaways arising from our work are: 1) validation of a RAG system is only feasible during operation, and 2) the robustness of a RAG system evolves rather than designed in at the start. We conclude with a list of potential research directions on RAG systems for the software engineering community.