Aussie AI
Table-Augmented Generation (TAG)
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Last Updated 18 July, 2025
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by David Spuler, Ph.D.
Table-Augmented Generation (TAG) is the use of a database query to extract rows of data from a table, which is used as input to a RAG system. This is similar to the return of database chunks from a vector database in a classic RAG system, but offers the advantages of greater personalization and immediacy via up-to-date databases. This architecture is similar to the use of data source plugins for an LLM application, but with a RAG spin on it.
See also more research on related areas:
- Model evaluation
- RAG architectures
- Vector databases
- RAG embedding models
- Advanced RAG architectures
- TAG
- RALM
Research on TAG
Research papers and articles on Table-Augmented Generation include:
- Shubham Sharma, September 2, 2024, Table-augmented generation shows promise for complex dataset querying, outperforms text-to-SQL, https://venturebeat.com/data-infrastructure/table-augmented-generation-shows-promise-for-complex-dataset-querying-outperforms-text-to-sql/
- Sreedevi Gogusetty, Dec 6, 2024, From RAG to TAG: Leveraging the Power of Table-Augmented Generation (TAG): A Leap Beyond Retrieval-Augmented Generation (RAG), https://ai.plainenglish.io/from-rag-to-tag-leveraging-the-power-of-table-augmented-generation-tag-a-leap-beyond-54d1cfadb994 (TAG for augmenting LLMs with queries from database tables, similar to data source plugins.)
- Tom Martin, Oct 15, 2024, From RAG to TAG: Exploring the Power of Table-Augmented Generation (TAG): A Leap Beyond Retrieval-Augmented Generation (RAG), https://ai.plainenglish.io/from-rag-to-tag-exploring-the-power-of-table-augmented-generation-tag-a-leap-beyond-b2c165309f63
- Asim Biswal, Liana Patel, Siddarth Jha, Amog Kamsetty, Shu Liu, Joseph E. Gonzalez, Carlos Guestrin, Matei Zaharia, 27 Aug 2024, Text2SQL is Not Enough: Unifying AI and Databases with TAG, https://arxiv.org/abs/2408.14717 Code: https://github.com/TAG-Research/TAG-Bench
- Zipeng Qiu, You Peng, Guangxin He, Binhang Yuan, Chen Wang, 29 Nov 2024, TQA-Bench: Evaluating LLMs for Multi-Table Question Answering with Scalable Context and Symbolic Extension, https://arxiv.org/abs/2411.19504
- Kyoungmin Kim, Anastasia Ailamaki, 23 Dec 2024, Trustworthy and Efficient LLMs Meet Databases, https://arxiv.org/abs/2412.18022
- Mayi Xu, Yunfeng Ning, Yongqi Li, Jianhao Chen, Jintao Wen, Yao Xiao, Shen Zhou, Birong Pan, Zepeng Bao, Xin Miao, Hankun Kang, Ke Sun, Tieyun Qian, 2 Jan 2025, Reasoning based on symbolic and parametric knowledge bases: a survey, https://arxiv.org/abs/2501.01030 (Extensive survey of reasoning from CoT to knowledge graphs to table-based reasoning.)
- Akssyd, Sep 27, 2024, Table-Augmented Generation (TAG): A Paradigm Shift in AI-Driven Data Queries, https://medium.com/@akssyd/table-augmented-generation-tag-a-paradigm-shift-in-ai-driven-data-queries-4f282fd59fd9
- Asim Biswal, Liana Patel, Siddarth Jha, Amog Kamsetty, Shu Liu, Joseph E. Gonzalez, Carlos Guestrin, Matei Zaharia, 22 June 2025, Introducing Table-Augmented Generation for Better Database Queries: TAG improves how natural language questions are answered using databases, https://scisimple.com/en/articles/2025-06-22-introducing-table-augmented-generation-for-better-database-queries--akgrrvn
- Richard W. L., 3 September 2024, Table-Augmented Generation: A Breakthrough in Complex Dataset Querying, https://www.techcloudup.com/2024/09/table-augmented-generation-breakthrough.html
- Tom Martin, Oct 14, 2024, Exploring the Power of Table-Augmented Generation (TAG): A Leap Beyond Retrieval-Augmented Generation (RAG), https://thomasjmartin.medium.com/exploring-the-power-of-table-augmented-generation-tag-a-leap-beyond-retrieval-augmented-16ac137ecb46
- kanishk khatter, Sep 17, 2024, From Queries to Insights: Revolutionize Your Data Strategy with Table Augmented Generation (TAG), https://medium.com/@kanishk.khatter/from-queries-to-insights-revolutionize-your-data-strategy-with-table-augmented-generation-tag-b9fb31006a52
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