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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.
* (7) Jiang, Z., Xu, F. F., Gao, L., Sun, Z., Liu, Q., Dwivedi-Yu, J., Yang, Y., Callan, J., and Neubig, G. Active retrieval augmented generation. ''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. 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., 他. GPT-4技術報告. arXiv preprint arXiv:2303.08774 (2023).
(3) Barnett, S., Kurniawan, S., Thudumu, S., Brannelly, Z., and Abdelrazek, M. 検索強化生成システムのエンジニアリングにおける7つの失敗点. arXiv preprint arXiv:2401.05856 (2024).
(4) Es, S., James, J., Espinosa-Anke, L., and Schockaert, S. Ragas: 検索強化生成の自動評価. arXiv preprint arXiv:2309.15217 (2023).
(5) Galitsky, B. エンタープライズチャットボットの開発. Springer, 2019.
(6) Glantz, W. 12のRAGの痛点と提案された解決策.
(7) Jiang, Z., Xu, F. F., Gao, L., Sun, Z., Liu, Q., Dwivedi-Yu, J., Yang, Y., Callan, J., and Neubig, G. アクティブ検索強化生成. 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., 他. 知識集約型NLPタスクのための検索強化生成. 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., 他. Chipnemo: チップ設計のためのドメイン適応型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., 他. Self-refine: 自己フィードバックによる反復的洗練. 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., 他. 拡張言語モデル: 調査. arXiv preprint arXiv:2302.07842 (2023).
(13) Rebedea, T., Dinu, R., Sreedhar, M., Parisien, C., and Cohen, J. Nemoガードレール: プログラム可能なレールを備えた制御可能で安全なLLMアプリケーションのためのツールキット. arXiv preprint arXiv:2310.10501 (2023).
(14) Saad-Falcon, J., Khattab, O., Potts, C., and Zaharia, M. Ares: 検索強化生成システムのための自動評価フレームワーク. arXiv preprint arXiv:2311.09476 (2023).
(15) Setty, S., Jijo, K., Chung, E., and Vidra, N. 金融文書におけるRAGベースの質問応答モデルの検索改善. arXiv preprint arXiv:2404.07221 (2024).
(16) Yao, S., Zhao, J., Yu, D., Du, N., Shafran, I., Narasimhan, K., and Cao, Y. React: 言語モデルにおける推論と行動のシナジー. 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. 情報検索のための大規模言語モデル: 調査. arXiv preprint arXiv:2308.07107 (2023).