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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., 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).

@@ Line 1: / Line 1: @@
 * <span id="bib.bib1">(1)</span> Langchain. ''https://github.com/langchain-ai''.
-* <span id="bib.bib2">(2)</span> Achiam, J., Adler, S., Agarwal, S., Ahmad, L., Akkaya, I., Aleman, F. L., Almeida, D., Altenschmidt, J., Altman, S., Anadkat, S., et al. Informe técnico de GPT-4. ''arXiv preprint arXiv:2303.08774'' (2023).
+* <span id="bib.bib2">(2)</span> 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).
-* <span id="bib.bib3">(3)</span> Barnett, S., Kurniawan, S., Thudumu, S., Brannelly, Z., y Abdelrazek, M. Siete puntos de fallo al diseñar un sistema de generación aumentada por recuperación. ''arXiv preprint arXiv:2401.05856'' (2024).
+* <span id="bib.bib3">(3)</span> 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).
-* <span id="bib.bib4">(4)</span> Es, S., James, J., Espinosa-Anke, L., y Schockaert, S. Ragas: Evaluación automatizada de generación aumentada por recuperación. ''arXiv preprint arXiv:2309.15217'' (2023).
+* <span id="bib.bib4">(4)</span> Es, S., James, J., Espinosa-Anke, L., and Schockaert, S. Ragas: Automated evaluation of retrieval augmented generation. ''arXiv preprint arXiv:2309.15217'' (2023).
-* <span id="bib.bib5">(5)</span> Galitsky, B. ''Desarrollando chatbots empresariales''. Springer, 2019.
+* <span id="bib.bib5">(5)</span> Galitsky, B. ''Developing enterprise chatbots''. Springer, 2019.
-* <span id="bib.bib6">(6)</span> Glantz, W. 12 puntos de dolor de rag y soluciones propuestas.
+* <span id="bib.bib6">(6)</span> Glantz, W. 12 rag pain points and proposed solutions.
-* <span id="bib.bib7">(7)</span> Jiang, Z., Xu, F. F., Gao, L., Sun, Z., Liu, Q., Dwivedi-Yu, J., Yang, Y., Callan, J., y Neubig, G. Generación aumentada por recuperación activa. ''arXiv preprint arXiv:2305.06983'' (2023).
+* <span id="bib.bib7">(7)</span> 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).
-* <span id="bib.bib8">(8)</span> Lewis, P., Perez, E., Piktus, A., Petroni, F., Karpukhin, V., Goyal, N., Küttler, H., Lewis, M., Yih, W.-t., Rocktäschel, T., et al. Generación aumentada por recuperación para tareas de PNL intensivas en conocimiento. ''Advances in Neural Information Processing Systems 33'' (2020), 9459–9474.
+* <span id="bib.bib8">(8)</span> 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.
 * <span id="bib.bib9">(9)</span> Liu, J. LlamaIndex. ''https://github.com/jerryjliu/llama_index''(2022).
-* <span id="bib.bib10">(10)</span> Liu, M., Ene, T.-D., Kirby, R., Cheng, C., Pinckney, N., Liang, R., Alben, J., Anand, H., Banerjee, S., Bayraktaroglu, I., et al. Chipnemo: LLMs adaptados al dominio para el diseño de chips. ''arXiv preprint arXiv:2311.00176'' (2023).
+* <span id="bib.bib10">(10)</span> 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).
-* <span id="bib.bib11">(11)</span> Madaan, A., Tandon, N., Gupta, P., Hallinan, S., Gao, L., Wiegreffe, S., Alon, U., Dziri, N., Prabhumoye, S., Yang, Y., et al. Auto-refinamiento: Refinamiento iterativo con retroalimentación propia. ''Advances in Neural Information Processing Systems 36'' (2024).
+* <span id="bib.bib11">(11)</span> 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).
-* <span id="bib.bib12">(12)</span> Mialon, G., Dessì, R., Lomeli, M., Nalmpantis, C., Pasunuru, R., Raileanu, R., Rozière, B., Schick, T., Dwivedi-Yu, J., Celikyilmaz, A., et al. Modelos de lenguaje aumentados: una encuesta. ''arXiv preprint arXiv:2302.07842'' (2023).
+* <span id="bib.bib12">(12)</span> 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).
-* <span id="bib.bib13">(13)</span> Rebedea, T., Dinu, R., Sreedhar, M., Parisien, C., y Cohen, J. Nemo guardrails: Un kit de herramientas para aplicaciones LLM controlables y seguras con rieles programables. ''arXiv preprint arXiv:2310.10501'' (2023).
+* <span id="bib.bib13">(13)</span> 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).
-* <span id="bib.bib14">(14)</span> Saad-Falcon, J., Khattab, O., Potts, C., y Zaharia, M. Ares: Un marco de evaluación automatizado para sistemas de generación aumentada por recuperación. ''arXiv preprint arXiv:2311.09476'' (2023).
+* <span id="bib.bib14">(14)</span> 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).
-* <span id="bib.bib15">(15)</span> Setty, S., Jijo, K., Chung, E., y Vidra, N. Mejorando la recuperación para modelos de respuesta a preguntas basados en rag en documentos financieros. ''arXiv preprint arXiv:2404.07221'' (2024).
+* <span id="bib.bib15">(15)</span> 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).
-* <span id="bib.bib16">(16)</span> Yao, S., Zhao, J., Yu, D., Du, N., Shafran, I., Narasimhan, K., y Cao, Y. React: Sinergizando el razonamiento y la actuación en modelos de lenguaje. ''arXiv preprint arXiv:2210.03629'' (2022).
+* <span id="bib.bib16">(16)</span> 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).
-* <span id="bib.bib17">(17)</span> Zhu, Y., Yuan, H., Wang, S., Liu, J., Liu, W., Deng, C., Dou, Z., y Wen, J.-R. Modelos de lenguaje grandes para la recuperación de información: una encuesta. ''arXiv preprint arXiv:2308.07107'' (2023).
+* <span id="bib.bib17">(17)</span> 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).