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- Vol. 5 Issue 3
- Article
Generative AI Development: A Practical Framework for Building, Evaluating and Deploying LLM-Based Applications
Authors
1 Professor, Shri Ambabai Talim Sanstha’s Sanjay Bhokare Group Of Institute, Miraj, Maharashtra, India. 2 3 4 5 6 Shri Ambabai Talim Sanstha’s Sanjay Bhokare Group Of Institute, Miraj, Maharashtra, India.
Article Information
- DOI
- No DOI
- Published In
- Volume 5, Issue 3 (2026)
- Published Online
- September-December 2026
- Pages
- 752-755
- Article Type
- Original Article
- Check for updates
- No DOI available for cross-reference
References
[1] A. Vaswani et al., “Attention is all you need,” in Advances in Neural Information Processing Systems (NeurIPS), 2017.
[2] J. Devlin, M. Chang, K. Lee, and K. Toutanova, “BERT: Pre-training of deep bidirectional transformers for language understanding,” in Proc. NAACL-HLT, 2019.
[3] T. B. Brown et al., “Language models are few-shot learners,” in NeurIPS, 2020.
[4] R. Bommasani et al., “On the opportunities and risks of foundation models,” arXiv:2108.07258, 2021.
[5] L. Ouyang et al., “Training language models to follow instructions with human feedback,” in NeurIPS, 2022.
[6] R. Rafailov et al., “Direct preference optimization: Your language model is secretly a reward model,” in NeurIPS, 2023.
[7] J. Wei et al., “Chain-of-thought prompting elicits reasoning in large language models,” in NeurIPS, 2022.
[8] V. Karpukhin et al., “Dense passage retrieval for open-domain question answering,” in Proc. EMNLP, 2020.
[9] P. Lewis et al., “Retrieval-augmented generation for knowledge-intensive NLP tasks,” in NeurIPS, 2020.
[10] Y. Gao et al., “Retrieval-augmented generation for large language models: A survey,” arXiv:2312.10997, 2023.
[11] S. Yao et al., “ReAct: Synergizing reasoning and acting in language models,” in Proc. ICLR, 2023.
[12] T. Schick et al., “Toolformer: Language models can teach themselves to use tools,” in NeurIPS, 2023.
[13] Anthropic, “Model Context Protocol,” [Online]. Available: https://modelcontextprotocol.io
[14] E. J. Hu et al., “LoRA: Low-rank adaptation of large language models,” in Proc. ICLR, 2022.
[15] T. Dettmers, A. Pagnoni, A. Holtzman, and L. Zettlemoyer, “QLoRA: Efficient finetuning of quantized LLMs,” in NeurIPS, 2023.
[16] T. Dao et al., “FlashAttention: Fast and memory-efficient exact attention with IO-awareness,” in NeurIPS, 2022.
[17] W. Kwon et al., “Efficient memory management for large language model serving with PagedAttention,” in Proc. ACM SOSP, 2023.
[18] P. Liang et al., “Holistic evaluation of language models,” Transactions on Machine Learning Research, 2023.
[19] L. Zheng et al., “Judging LLM-as-a-judge with MT-Bench and Chatbot Arena,” in NeurIPS Datasets and Benchmarks Track, 2023.
[20] Z. Ji et al., “Survey of hallucination in natural language generation,” ACM Computing Surveys, vol. 55, no. 12, 2023.
[21] F. Perez and I. Ribeiro, “Ignore previous prompt: Attack techniques for language models,” arXiv:2211.09527, 2022.
[22] K. Greshake et al., “Not what you’ve signed up for: Compromising real-world LLM-integrated applications with indirect prompt injection,” in Proc. ACM AISec, 2023.
[2] J. Devlin, M. Chang, K. Lee, and K. Toutanova, “BERT: Pre-training of deep bidirectional transformers for language understanding,” in Proc. NAACL-HLT, 2019.
[3] T. B. Brown et al., “Language models are few-shot learners,” in NeurIPS, 2020.
[4] R. Bommasani et al., “On the opportunities and risks of foundation models,” arXiv:2108.07258, 2021.
[5] L. Ouyang et al., “Training language models to follow instructions with human feedback,” in NeurIPS, 2022.
[6] R. Rafailov et al., “Direct preference optimization: Your language model is secretly a reward model,” in NeurIPS, 2023.
[7] J. Wei et al., “Chain-of-thought prompting elicits reasoning in large language models,” in NeurIPS, 2022.
[8] V. Karpukhin et al., “Dense passage retrieval for open-domain question answering,” in Proc. EMNLP, 2020.
[9] P. Lewis et al., “Retrieval-augmented generation for knowledge-intensive NLP tasks,” in NeurIPS, 2020.
[10] Y. Gao et al., “Retrieval-augmented generation for large language models: A survey,” arXiv:2312.10997, 2023.
[11] S. Yao et al., “ReAct: Synergizing reasoning and acting in language models,” in Proc. ICLR, 2023.
[12] T. Schick et al., “Toolformer: Language models can teach themselves to use tools,” in NeurIPS, 2023.
[13] Anthropic, “Model Context Protocol,” [Online]. Available: https://modelcontextprotocol.io
[14] E. J. Hu et al., “LoRA: Low-rank adaptation of large language models,” in Proc. ICLR, 2022.
[15] T. Dettmers, A. Pagnoni, A. Holtzman, and L. Zettlemoyer, “QLoRA: Efficient finetuning of quantized LLMs,” in NeurIPS, 2023.
[16] T. Dao et al., “FlashAttention: Fast and memory-efficient exact attention with IO-awareness,” in NeurIPS, 2022.
[17] W. Kwon et al., “Efficient memory management for large language model serving with PagedAttention,” in Proc. ACM SOSP, 2023.
[18] P. Liang et al., “Holistic evaluation of language models,” Transactions on Machine Learning Research, 2023.
[19] L. Zheng et al., “Judging LLM-as-a-judge with MT-Bench and Chatbot Arena,” in NeurIPS Datasets and Benchmarks Track, 2023.
[20] Z. Ji et al., “Survey of hallucination in natural language generation,” ACM Computing Surveys, vol. 55, no. 12, 2023.
[21] F. Perez and I. Ribeiro, “Ignore previous prompt: Attack techniques for language models,” arXiv:2211.09527, 2022.
[22] K. Greshake et al., “Not what you’ve signed up for: Compromising real-world LLM-integrated applications with indirect prompt injection,” in Proc. ACM AISec, 2023.
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