Seven Failure Points When Engineering a Retrieval Augmented Generation System
Fuente:
arXiv
Saved in:
| Main Authors: | Barnett, Scott, Kurniawan, Stefanus, Thudumu, Srikanth, Brannelly, Zach, Abdelrazek, Mohamed |
|---|---|
| Format: | Preprint |
| Published: |
2024
|
| Subjects: | |
| Online Access: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
LLMs for Test Input Generation for Semantic Caches
by: Rasool, Zafaryab, et al.
Published: (2024)
by: Rasool, Zafaryab, et al.
Published: (2024)
Large language models for generating rules, yay or nay?
by: Sivasothy, Shangeetha, et al.
Published: (2024)
by: Sivasothy, Shangeetha, et al.
Published: (2024)
TaskEval: Synthesised Evaluation for Foundation-Model Tasks
by: Widanapathiranage, Dilani, et al.
Published: (2025)
by: Widanapathiranage, Dilani, et al.
Published: (2025)
ML-On-Rails: Safeguarding Machine Learning Models in Software Systems A Case Study
by: Abdelkader, Hala, et al.
Published: (2024)
by: Abdelkader, Hala, et al.
Published: (2024)
Fine-Tuning or Fine-Failing? Debunking Performance Myths in Large Language Models
by: Barnett, Scott, et al.
Published: (2024)
by: Barnett, Scott, et al.
Published: (2024)
Advanced System Integration: Analyzing OpenAPI Chunking for Retrieval-Augmented Generation
by: Pesl, Robin D., et al.
Published: (2024)
by: Pesl, Robin D., et al.
Published: (2024)
Retrieval-Augmented Test Generation: How Far Are We?
by: Shin, Jiho, et al.
Published: (2024)
by: Shin, Jiho, et al.
Published: (2024)
Retrieval-Augmented Generation for Service Discovery: Chunking Strategies and Benchmarking
by: Pesl, Robin D., et al.
Published: (2025)
by: Pesl, Robin D., et al.
Published: (2025)
HCAG: Hierarchical Abstraction and Retrieval-Augmented Generation on Theoretical Repositories with LLMs
by: Wu, Yusen, et al.
Published: (2026)
by: Wu, Yusen, et al.
Published: (2026)
Understanding the Fundamental Design Decisions of Retrieval-Augmented Generation Systems
by: Zhao, Shengming, et al.
Published: (2024)
by: Zhao, Shengming, et al.
Published: (2024)
Model-Driven Quantum Code Generation Using Large Language Models and Retrieval-Augmented Generation
by: Siavash, Nazanin, et al.
Published: (2025)
by: Siavash, Nazanin, et al.
Published: (2025)
Optimizing Code Runtime Performance through Context-Aware Retrieval-Augmented Generation
by: Acharya, Manish, et al.
Published: (2025)
by: Acharya, Manish, et al.
Published: (2025)
RAG-Verus: Repository-Level Program Verification with LLMs using Retrieval Augmented Generation
by: Zhong, Sicheng, et al.
Published: (2025)
by: Zhong, Sicheng, et al.
Published: (2025)
Enhancing Code Consistency in AI Research with Large Language Models and Retrieval-Augmented Generation
by: Keshri, Rajat, et al.
Published: (2025)
by: Keshri, Rajat, et al.
Published: (2025)
Enhancing Code Translation in Language Models with Few-Shot Learning via Retrieval-Augmented Generation
by: Bhattarai, Manish, et al.
Published: (2024)
by: Bhattarai, Manish, et al.
Published: (2024)
ReCode: Improving LLM-based Code Repair with Fine-Grained Retrieval-Augmented Generation
by: Zhao, Yicong, et al.
Published: (2025)
by: Zhao, Yicong, et al.
Published: (2025)
RAG-MCP: Mitigating Prompt Bloat in LLM Tool Selection via Retrieval-Augmented Generation
by: Gan, Tiantian, et al.
Published: (2025)
by: Gan, Tiantian, et al.
Published: (2025)
Debugging the Debuggers: Failure-Anchored Structured Recovery for Software Engineering Agents
by: Zhao, Chenyu, et al.
Published: (2026)
by: Zhao, Chenyu, et al.
Published: (2026)
RAILS: Retrieval-Augmented Intelligence for Learning Software Development
by: Abdullah, Wali Mohammad, et al.
Published: (2025)
by: Abdullah, Wali Mohammad, et al.
Published: (2025)
PropertyGPT: LLM-driven Formal Verification of Smart Contracts through Retrieval-Augmented Property Generation
by: Liu, Ye, et al.
Published: (2024)
by: Liu, Ye, et al.
Published: (2024)
Enhancing Cross-Language Code Translation via Task-Specific Embedding Alignment in Retrieval-Augmented Generation
by: Bhattarai, Manish, et al.
Published: (2024)
by: Bhattarai, Manish, et al.
Published: (2024)
CodeGRAG: Bridging the Gap between Natural Language and Programming Language via Graphical Retrieval Augmented Generation
by: Du, Kounianhua, et al.
Published: (2024)
by: Du, Kounianhua, et al.
Published: (2024)
ARCS: Agentic Retrieval-Augmented Code Synthesis with Iterative Refinement
by: Bhattarai, Manish, et al.
Published: (2025)
by: Bhattarai, Manish, et al.
Published: (2025)
RAG4Tickets: AI-Powered Ticket Resolution via Retrieval-Augmented Generation on JIRA and GitHub Data
by: Baqar, Mohammad
Published: (2025)
by: Baqar, Mohammad
Published: (2025)
Developing Retrieval Augmented Generation (RAG) based LLM Systems from PDFs: An Experience Report
by: Khan, Ayman Asad, et al.
Published: (2024)
by: Khan, Ayman Asad, et al.
Published: (2024)
When Prompt Engineering Meets Software Engineering: CNL-P as Natural and Robust "APIs'' for Human-AI Interaction
by: Xing, Zhenchang, et al.
Published: (2025)
by: Xing, Zhenchang, et al.
Published: (2025)
LibRec: Benchmarking Retrieval-Augmented LLMs for Library Migration Recommendations
by: Han, Junxiao, et al.
Published: (2025)
by: Han, Junxiao, et al.
Published: (2025)
GenAI-powered Multi-Agent Paradigm for Smart Urban Mobility: Opportunities and Challenges for Integrating Large Language Models (LLMs) and Retrieval-Augmented Generation (RAG) with Intelligent Transportation Systems
by: Xu, Haowen, et al.
Published: (2024)
by: Xu, Haowen, et al.
Published: (2024)
Document Retrieval Augmented Fine-Tuning (DRAFT) for safety-critical software assessments
by: Bolton, Regan, et al.
Published: (2025)
by: Bolton, Regan, et al.
Published: (2025)
Efficient Failure Management for Multi-Agent Systems with Reasoning Trace Representation
by: Zhang, Lingzhe, et al.
Published: (2026)
by: Zhang, Lingzhe, et al.
Published: (2026)
P4OMP: Retrieval-Augmented Prompting for OpenMP Parallelism in Serial Code
by: Abdullah, Wali Mohammad, et al.
Published: (2025)
by: Abdullah, Wali Mohammad, et al.
Published: (2025)
Engineering AI Judge Systems
by: Lin, Jiahuei, et al.
Published: (2024)
by: Lin, Jiahuei, et al.
Published: (2024)
From Code-Centric to Intent-Centric Software Engineering: A Reflexive Thematic Analysis of Generative AI, Agentic Systems, and Engineering Accountability
by: De La Cruz, Elyson
Published: (2026)
by: De La Cruz, Elyson
Published: (2026)
Lost in Transcription: How Speech-to-Text Errors Derail Code Understanding
by: Havare, Jayant, et al.
Published: (2026)
by: Havare, Jayant, et al.
Published: (2026)
Robustness and Reasoning Fidelity of Large Language Models in Long-Context Code Question Answering
by: Maharaj, Kishan, et al.
Published: (2026)
by: Maharaj, Kishan, et al.
Published: (2026)
RTLRepoCoder: Repository-Level RTL Code Completion through the Combination of Fine-Tuning and Retrieval Augmentation
by: Wu, Peiyang, et al.
Published: (2025)
by: Wu, Peiyang, et al.
Published: (2025)
Towards Requirements Engineering for RAG Systems
by: Sporsem, Tor, et al.
Published: (2025)
by: Sporsem, Tor, et al.
Published: (2025)
Evaluating Retrieval-Augmented Generation Variants for Natural Language-Based SQL and API Call Generation
by: Marketsmüller, Michael, et al.
Published: (2026)
by: Marketsmüller, Michael, et al.
Published: (2026)
FailureMem: A Failure-Aware Multimodal Framework for Autonomous Software Repair
by: Ma, Ruize, et al.
Published: (2026)
by: Ma, Ruize, et al.
Published: (2026)
Towards Automated Smart Contract Generation: Evaluation, Benchmarking, and Retrieval-Augmented Repair
by: Chen, Zaoyu, et al.
Published: (2025)
by: Chen, Zaoyu, et al.
Published: (2025)
Similar Items
-
LLMs for Test Input Generation for Semantic Caches
by: Rasool, Zafaryab, et al.
Published: (2024) -
Large language models for generating rules, yay or nay?
by: Sivasothy, Shangeetha, et al.
Published: (2024) -
TaskEval: Synthesised Evaluation for Foundation-Model Tasks
by: Widanapathiranage, Dilani, et al.
Published: (2025) -
ML-On-Rails: Safeguarding Machine Learning Models in Software Systems A Case Study
by: Abdelkader, Hala, et al.
Published: (2024) -
Fine-Tuning or Fine-Failing? Debunking Performance Myths in Large Language Models
by: Barnett, Scott, et al.
Published: (2024)