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* (1) Langchain. ''https://github.com/langchain-ai''.
* (2) Achiam, J., Adler, S., Agarwal, S., Ahmad, L., Akkaya, I., Aleman, F. L., Almeida, D., Altenschmidt, J., Altman, S., Anadkat, S., et al. GPT-4 technical report. ''arXiv preprint arXiv:2303.08774'' (2023).
* (3) Barnett, S., Kurniawan, S., Thudumu, S., Brannelly, Z., and Abdelrazek, M. Seven failure points when engineering a retrieval augmented generation system. ''arXiv preprint arXiv:2401.05856'' (2024).
* (4) Es, S., James, J., Espinosa-Anke, L., and Schockaert, S. Ragas: Automated evaluation of retrieval augmented generation. ''arXiv preprint arXiv:2309.15217'' (2023).
* (5) Galitsky, B. ''Developing enterprise chatbots''. Springer, 2019.
* (6) Glantz, W. 12 rag pain points and proposed solutions.
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* (8) Lewis, P., Perez, E., Piktus, A., Petroni, F., Karpukhin, V., Goyal, N., Küttler, H., Lewis, M., Yih, W.-t., Rocktäschel, T., et al. Retrieval-augmented generation for knowledge-intensive nlp tasks. ''Advances in Neural Information Processing Systems 33'' (2020), 9459–9474.
* (9) Liu, J. LlamaIndex. ''https://github.com/jerryjliu/llama_index''(2022).
* (10) Liu, M., Ene, T.-D., Kirby, R., Cheng, C., Pinckney, N., Liang, R., Alben, J., Anand, H., Banerjee, S., Bayraktaroglu, I., et al. Chipnemo: Domain-adapted llms for chip design. ''arXiv preprint arXiv:2311.00176'' (2023).
* (11) Madaan, A., Tandon, N., Gupta, P., Hallinan, S., Gao, L., Wiegreffe, S., Alon, U., Dziri, N., Prabhumoye, S., Yang, Y., et al. Self-refine: Iterative refinement with self-feedback. ''Advances in Neural Information Processing Systems 36'' (2024).
* (12) Mialon, G., Dessì, R., Lomeli, M., Nalmpantis, C., Pasunuru, R., Raileanu, R., Rozière, B., Schick, T., Dwivedi-Yu, J., Celikyilmaz, A., et al. Augmented language models: a survey. ''arXiv preprint arXiv:2302.07842'' (2023).
* (13) Rebedea, T., Dinu, R., Sreedhar, M., Parisien, C., and Cohen, J. Nemo guardrails: A toolkit for controllable and safe llm applications with programmable rails. ''arXiv preprint arXiv:2310.10501'' (2023).
* (14) Saad-Falcon, J., Khattab, O., Potts, C., and Zaharia, M. Ares: An automated evaluation framework for retrieval-augmented generation systems. ''arXiv preprint arXiv:2311.09476'' (2023).
* (15) Setty, S., Jijo, K., Chung, E., and Vidra, N. Improving retrieval for rag based question answering models on financial documents. ''arXiv preprint arXiv:2404.07221'' (2024).
* (16) Yao, S., Zhao, J., Yu, D., Du, N., Shafran, I., Narasimhan, K., and Cao, Y. React: Synergizing reasoning and acting in language models. ''arXiv preprint arXiv:2210.03629'' (2022).
* (17) Zhu, Y., Yuan, H., Wang, S., Liu, J., Liu, W., Deng, C., Dou, Z., and Wen, J.-R. Large language models for information retrieval: A survey. ''arXiv preprint arXiv:2308.07107'' (2023).

(1) Langchain. https://github.com/langchain-ai.
(2) Achiam, J., Adler, S., Agarwal, S., Ahmad, L., Akkaya, I., Aleman, F. L., Almeida, D., Altenschmidt, J., Altman, S., Anadkat, S., et al. GPT-4 기술 보고서. arXiv preprint arXiv:2303.08774 (2023).
(3) Barnett, S., Kurniawan, S., Thudumu, S., Brannelly, Z., and Abdelrazek, M. 검색 증강 생성 시스템을 설계할 때의 일곱 가지 실패 지점. arXiv preprint arXiv:2401.05856 (2024).
(4) Es, S., James, J., Espinosa-Anke, L., and Schockaert, S. Ragas: 검색 증강 생성의 자동 평가. arXiv preprint arXiv:2309.15217 (2023).
(5) Galitsky, B. 기업용 챗봇 개발. Springer, 2019.
(6) Glantz, W. 12개의 rag 문제점과 제안된 해결책.
(7) Jiang, Z., Xu, F. F., Gao, L., Sun, Z., Liu, Q., Dwivedi-Yu, J., Yang, Y., Callan, J., and Neubig, G. 능동적 검색 증강 생성. arXiv preprint arXiv:2305.06983 (2023).
(8) Lewis, P., Perez, E., Piktus, A., Petroni, F., Karpukhin, V., Goyal, N., Küttler, H., Lewis, M., Yih, W.-t., Rocktäschel, T., et al. 지식 집약적 NLP 작업을 위한 검색 증강 생성. Advances in Neural Information Processing Systems 33 (2020), 9459–9474.
(9) Liu, J. LlamaIndex. https://github.com/jerryjliu/llama_index(2022).
(10) Liu, M., Ene, T.-D., Kirby, R., Cheng, C., Pinckney, N., Liang, R., Alben, J., Anand, H., Banerjee, S., Bayraktaroglu, I., et al. Chipnemo: 칩 설계를 위한 도메인 적응 LLM. arXiv preprint arXiv:2311.00176 (2023).
(11) Madaan, A., Tandon, N., Gupta, P., Hallinan, S., Gao, L., Wiegreffe, S., Alon, U., Dziri, N., Prabhumoye, S., Yang, Y., et al. Self-refine: 자기 피드백을 통한 반복적 정제. Advances in Neural Information Processing Systems 36 (2024).
(12) Mialon, G., Dessì, R., Lomeli, M., Nalmpantis, C., Pasunuru, R., Raileanu, R., Rozière, B., Schick, T., Dwivedi-Yu, J., Celikyilmaz, A., et al. 증강 언어 모델: 설문 조사. arXiv preprint arXiv:2302.07842 (2023).
(13) Rebedea, T., Dinu, R., Sreedhar, M., Parisien, C., and Cohen, J. Nemo guardrails: 프로그래머블 레일을 사용한 제어 가능하고 안전한 LLM 응용 프로그램을 위한 도구 키트. arXiv preprint arXiv:2310.10501 (2023).
(14) Saad-Falcon, J., Khattab, O., Potts, C., and Zaharia, M. Ares: 검색 증강 생성 시스템을 위한 자동 평가 프레임워크. arXiv preprint arXiv:2311.09476 (2023).
(15) Setty, S., Jijo, K., Chung, E., and Vidra, N. 금융 문서에 대한 RAG 기반 질문 응답 모델의 검색 개선. arXiv preprint arXiv:2404.07221 (2024).
(16) Yao, S., Zhao, J., Yu, D., Du, N., Shafran, I., Narasimhan, K., and Cao, Y. React: 언어 모델에서 추론과 행동의 시너지. arXiv preprint arXiv:2210.03629 (2022).
(17) Zhu, Y., Yuan, H., Wang, S., Liu, J., Liu, W., Deng, C., Dou, Z., and Wen, J.-R. 정보 검색을 위한 대형 언어 모델: 설문 조사. arXiv preprint arXiv:2308.07107 (2023).