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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., et al. GPT-4 technical report. arXiv preprint arXiv:2303.08774 (2023).
(3) Barnett, S., Kurniawan, S., Thudumu, S., Brannelly, Z., e 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., e 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., e 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., e 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., e 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., e 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., e 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., e Wen, J.-R. Large language models for information retrieval: A survey. arXiv preprint arXiv:2308.07107 (2023).