Previously on @thatjeffsmith... NL2SQL is cool. Reports are trustworthy. You can have both!
NL2SQL is cool. Reports are trustworthy. You can have both!
www.thatjeffsmith.com
November 9, 2026 at 9:41 PM
Previously on @thatjeffsmith... NL2SQL is cool. Reports are trustworthy. You can have both!
Previously on @thatjeffsmith... Using SQLcl MCP to generate and consume annotations for better NL2SQL
Using SQLcl MCP to generate and consume annotations for better NL2SQL
www.thatjeffsmith.com
October 31, 2026 at 12:38 PM
Previously on @thatjeffsmith... Using SQLcl MCP to generate and consume annotations for better NL2SQL
573 - Smart answer accuracy on SQL jumped from 78% to 90%. The data agent stopped writing queries in one pass and started exploring schemas and checking filter values first.
youtu.be/9_ssMBFK9GE
#microsoftfabric #dataagent #nl2sql #ai
youtu.be/9_ssMBFK9GE
#microsoftfabric #dataagent #nl2sql #ai
October 31, 2026 at 9:32 AM
573 - Smart answer accuracy on SQL jumped from 78% to 90%. The data agent stopped writing queries in one pass and started exploring schemas and checking filter values first.
youtu.be/9_ssMBFK9GE
#microsoftfabric #dataagent #nl2sql #ai
youtu.be/9_ssMBFK9GE
#microsoftfabric #dataagent #nl2sql #ai
Advanced NL2SQL and advanced DAX generation in Fabric data agents (Generally Available) by Misha Desai
https://community.fabric.microsoft.com/t5/Fabric-Updates-Blog/Advanced-NL2SQL-and-advanced-DAX-generation-in-Fabric-data/ba-p/5374654?utm_source=bluesky&utm_medium=social&utm_campaign=newsletter
https://community.fabric.microsoft.com/t5/Fabric-Updates-Blog/Advanced-NL2SQL-and-advanced-DAX-generation-in-Fabric-data/ba-p/5374654?utm_source=bluesky&utm_medium=social&utm_campaign=newsletter
Advanced NL2SQL and advanced DAX generation in Fabric data agents (Generally Available) by Misha Desai
community.fabric.microsoft.com
October 29, 2026 at 3:45 PM
Advanced NL2SQL and advanced DAX generation in Fabric data agents (Generally Available) by Misha Desai
https://community.fabric.microsoft.com/t5/Fabric-Updates-Blog/Advanced-NL2SQL-and-advanced-DAX-generation-in-Fabric-data/ba-p/5374654?utm_source=bluesky&utm_medium=social&utm_campaign=newsletter
https://community.fabric.microsoft.com/t5/Fabric-Updates-Blog/Advanced-NL2SQL-and-advanced-DAX-generation-in-Fabric-data/ba-p/5374654?utm_source=bluesky&utm_medium=social&utm_campaign=newsletter
Advanced NL2SQL and advanced DAX generation in Fabric data agents (Generally Available) by Misha Desai
https://community.fabric.microsoft.com/t5/Fabric-Updates-Blog/Advanced-NL2SQL-and-advanced-DAX-generation-in-Fabric-data/ba-p/5374654?utm_source=bluesky&utm_medium=social&utm_campaign=newsletter
https://community.fabric.microsoft.com/t5/Fabric-Updates-Blog/Advanced-NL2SQL-and-advanced-DAX-generation-in-Fabric-data/ba-p/5374654?utm_source=bluesky&utm_medium=social&utm_campaign=newsletter
Advanced NL2SQL and advanced DAX generation in Fabric data agents (Generally Available) by Misha Desai
community.fabric.microsoft.com
October 29, 2026 at 3:45 PM
Advanced NL2SQL and advanced DAX generation in Fabric data agents (Generally Available) by Misha Desai
https://community.fabric.microsoft.com/t5/Fabric-Updates-Blog/Advanced-NL2SQL-and-advanced-DAX-generation-in-Fabric-data/ba-p/5374654?utm_source=bluesky&utm_medium=social&utm_campaign=newsletter
https://community.fabric.microsoft.com/t5/Fabric-Updates-Blog/Advanced-NL2SQL-and-advanced-DAX-generation-in-Fabric-data/ba-p/5374654?utm_source=bluesky&utm_medium=social&utm_campaign=newsletter
Agent と RAG と NL2SQL の処理をたどる Oracle Autonomous AI Database に保存する Tracing の仕組み
#rag #oci #生成AI #tracing #OracleDatabase
#rag #oci #生成AI #tracing #OracleDatabase
Agent と RAG と NL2SQL の処理をたどる Oracle Autonomous AI Database に保存する Tracing の仕組み - Qiita
初めに AI の回答を待っているとき、こんなことはないでしょうか。 回答が返ってくるまで、1 分かかった。 文書の検索が遅いのか、AI が文章を作っているのか、SQL の実行で待っているのか。 「処理中」としか表示されないので、どこを調べればよいか分からない。 Age...
qiita.com
October 28, 2026 at 3:20 AM
Agent と RAG と NL2SQL の処理をたどる Oracle Autonomous AI Database に保存する Tracing の仕組み
#rag #oci #生成AI #tracing #OracleDatabase
#rag #oci #生成AI #tracing #OracleDatabase
Anjali Vemula, Akshat Dasula: NL2SQL with Iterative Self-Refinement https://arxiv.org/abs/2610.22968 https://arxiv.org/pdf/2610.22968 https://arxiv.org/html/2610.22968
October 19, 2026 at 6:40 AM
Anjali Vemula, Akshat Dasula: NL2SQL with Iterative Self-Refinement https://arxiv.org/abs/2610.22968 https://arxiv.org/pdf/2610.22968 https://arxiv.org/html/2610.22968
Previously on @thatjeffsmith... NL2SQL is cool. Reports are trustworthy. You can have both!
NL2SQL is cool. Reports are trustworthy. You can have both!
Previously on @thatjeffsmith... NL2SQL is cool. Reports are trustworthy. You can have both!
www.thatjeffsmith.com
October 11, 2026 at 4:41 AM
Previously on @thatjeffsmith... NL2SQL is cool. Reports are trustworthy. You can have both!
Why, Where, How: Taxonomy-guided Error Grounding for Code Repair in NL2SQL
Suchan Lee et al.
#arXiv #cs.AI
Suchan Lee et al.
#arXiv #cs.AI
Why, Where, How: Taxonomy-guided Error Grounding for Code Repair in NL2SQL
SQL queries that large language models write from natural language questions can execute successfully yet produce incorrect results, so execution alone does not reveal what to fix. An error taxonomy says why the query is wrong, but not where to look or how to change it. Existing methods can guide S…
arxiv.org
October 7, 2026 at 7:46 PM
Autonomous AI Lakehouseのデータを Ontologyでつないで Private Agent Factoryから NL2SQL＋RAG＋Knowledge Graphしてみてみた
#Lakehouse #NL2SQL #rag #KnowledgeGraph #PAF
#Lakehouse #NL2SQL #rag #KnowledgeGraph #PAF
Autonomous AI Lakehouseのデータを Ontologyでつないで Private Agent Factoryから NL2SQL＋RAG＋Knowledge Graphしてみてみた - Qiita
前回の音楽フェス運営Agent に、機材・製造Lot・Stage・Task・Documentの意味と関係を加えてみました。今回は、一つの障害から、ほかに確認すべき機材と、その対応の根拠までたどるところを試します。まず、実際に確認できた結果から見ていきます。 ■ Onto...
qiita.com
October 8, 2026 at 2:20 AM
Autonomous AI Lakehouseのデータを Ontologyでつないで Private Agent Factoryから NL2SQL＋RAG＋Knowledge Graphしてみてみた
#Lakehouse #NL2SQL #rag #KnowledgeGraph #PAF
#Lakehouse #NL2SQL #rag #KnowledgeGraph #PAF
今日のQiitaトレンド
Autonomous AI Lakehouseのデータを Ontologyでつないで Private Agent Factoryから NL2SQL＋RAG＋Knowledge Graphしてみてみた
音楽フェス運営AIエージェントにオントロジーとナレッジグラフを導入し、障害発生時の影響範囲や対応根拠を特定する手法を解説しています。
NL2SQL、ナレッジグラフ、RAGを組み合わせることで、同一ロットの機材特定から未完了タスクや運用手順書の確認までを一括して行い、根拠に基づいた迅速な対応を可能にする仕組みを構築しています。
Autonomous AI Lakehouseのデータを Ontologyでつないで Private Agent Factoryから NL2SQL＋RAG＋Knowledge Graphしてみてみた
音楽フェス運営AIエージェントにオントロジーとナレッジグラフを導入し、障害発生時の影響範囲や対応根拠を特定する手法を解説しています。
NL2SQL、ナレッジグラフ、RAGを組み合わせることで、同一ロットの機材特定から未完了タスクや運用手順書の確認までを一括して行い、根拠に基づいた迅速な対応を可能にする仕組みを構築しています。
Autonomous AI Lakehouseのデータを Ontologyでつないで Private Agent Factoryから NL2SQL＋RAG＋Knowledge Graphしてみてみた #rag - Qiita
前回の音楽フェス運営Agent に、機材・製造Lot・Stage・Task・Documentの意味と関係を加えてみました。今回は、一つの障害から、ほかに確認すべき機材と、その対応の根拠までたどるところを試します。まず、実際に確認できた結果から見ていきます。 ■ Onto...
qiita.com
October 8, 2026 at 1:50 AM
今日のQiitaトレンド
Autonomous AI Lakehouseのデータを Ontologyでつないで Private Agent Factoryから NL2SQL＋RAG＋Knowledge Graphしてみてみた
音楽フェス運営AIエージェントにオントロジーとナレッジグラフを導入し、障害発生時の影響範囲や対応根拠を特定する手法を解説しています。
NL2SQL、ナレッジグラフ、RAGを組み合わせることで、同一ロットの機材特定から未完了タスクや運用手順書の確認までを一括して行い、根拠に基づいた迅速な対応を可能にする仕組みを構築しています。
Autonomous AI Lakehouseのデータを Ontologyでつないで Private Agent Factoryから NL2SQL＋RAG＋Knowledge Graphしてみてみた
音楽フェス運営AIエージェントにオントロジーとナレッジグラフを導入し、障害発生時の影響範囲や対応根拠を特定する手法を解説しています。
NL2SQL、ナレッジグラフ、RAGを組み合わせることで、同一ロットの機材特定から未完了タスクや運用手順書の確認までを一括して行い、根拠に基づいた迅速な対応を可能にする仕組みを構築しています。
Suchan Lee, Woomin Song, Hwanjo Yu, Sangwoo Mo: Why, Where, How: Taxonomy-guided Error Grounding for Code Repair in NL2SQL https://arxiv.org/abs/2610.05060 https://arxiv.org/pdf/2610.05060 https://arxiv.org/html/2610.05060
October 7, 2026 at 6:40 AM
Suchan Lee, Woomin Song, Hwanjo Yu, Sangwoo Mo: Why, Where, How: Taxonomy-guided Error Grounding for Code Repair in NL2SQL https://arxiv.org/abs/2610.05060 https://arxiv.org/pdf/2610.05060 https://arxiv.org/html/2610.05060
Why, Where, How: Taxonomy-guided Error Grounding for Code Repair in NL2SQL
Suchan Lee et al.
#arXiv #cs.AI
Suchan Lee et al.
#arXiv #cs.AI
Why, Where, How: Taxonomy-guided Error Grounding for Code Repair in NL2SQL
SQL queries that large language models write from natural language questions can execute successfully yet produce incorrect results, so execution alone does not reveal what to fix. An error taxonomy says why the query is wrong, but not where to look or how to change it. Existing methods can guide S…
arxiv.org
October 6, 2026 at 8:31 PM
Why, Where, How: Taxonomy-guided Error Grounding for Code Repair in NL2SQL
Suchan Lee et al.
#arXiv #cs.AI
Suchan Lee et al.
#arXiv #cs.AI
Why, Where, How: Taxonomy-guided Error Grounding for Code Repair in NL2SQL
SQL queries that large language models write from natural language questions can execute successfully yet produce incorrect results, so execution alone does not reveal what to fix. An error taxonomy says why the query is wrong, but not where to look or how to change it. Existing methods can guide S…
arxiv.org
October 6, 2026 at 7:21 PM
Suchan Lee, Woomin Song, Hwanjo Yu, Sangwoo Mo: Why, Where, How: Taxonomy-guided Error Grounding for Code Repair in NL2SQL https://arxiv.org/abs/2610.05060 https://arxiv.org/pdf/2610.05060 https://arxiv.org/html/2610.05060
October 6, 2026 at 6:40 AM
Suchan Lee, Woomin Song, Hwanjo Yu, Sangwoo Mo: Why, Where, How: Taxonomy-guided Error Grounding for Code Repair in NL2SQL https://arxiv.org/abs/2610.05060 https://arxiv.org/pdf/2610.05060 https://arxiv.org/html/2610.05060
FALCON offers a framework for generating high-complexity NL-to-SQL training data, merging linguistic richness with ambiguity awareness, enabling organizations to produce realistic datasets locally at lower costs, improving performance on complex queries. https://arxiv.org/abs/2610.03625
FALCON: A Model and Dataset Agnostic Framework for Synthetic Data Generation for NL2SQL Pairs
ArXiv link for FALCON: A Model and Dataset Agnostic Framework for Synthetic Data Generation for NL2SQL Pairs
arxiv.org
October 6, 2026 at 4:00 AM
FALCON offers a framework for generating high-complexity NL-to-SQL training data, merging linguistic richness with ambiguity awareness, enabling organizations to produce realistic datasets locally at lower costs, improving performance on complex queries. https://arxiv.org/abs/2610.03625
Darian Lee, Shannon Rumsey, Jack St. Clair, Xinyi Tang, Aditya Bansal, Yuanming Shi: FALCON: A Model and Dataset Agnostic Framework for Synthetic Data Generation for NL2SQL Pairs https://arxiv.org/abs/2610.03625 https://arxiv.org/pdf/2610.03625 https://arxiv.org/html/2610.03625
October 5, 2026 at 6:40 AM
Darian Lee, Shannon Rumsey, Jack St. Clair, Xinyi Tang, Aditya Bansal, Yuanming Shi: FALCON: A Model and Dataset Agnostic Framework for Synthetic Data Generation for NL2SQL Pairs https://arxiv.org/abs/2610.03625 https://arxiv.org/pdf/2610.03625 https://arxiv.org/html/2610.03625
Previously on @thatjeffsmith... Using SQLcl MCP to generate and consume annotations for better NL2SQL
Using SQLcl MCP to generate and consume annotations for better NL2SQL
Previously on @thatjeffsmith... Using SQLcl MCP to generate and consume annotations for better NL2SQL
www.thatjeffsmith.com
September 30, 2026 at 4:29 PM
Previously on @thatjeffsmith... Using SQLcl MCP to generate and consume annotations for better NL2SQL
Previously on @thatjeffsmith... NL2SQL: Let’s put Opus 4.1 to task on my NETFLIX data
NL2SQL: Let’s put Opus 4.1 to task on my NETFLIX data
Previously on @thatjeffsmith... NL2SQL: Let’s put Opus 4.1 to task on my NETFLIX data
www.thatjeffsmith.com
September 30, 2026 at 4:40 AM
Previously on @thatjeffsmith... NL2SQL: Let’s put Opus 4.1 to task on my NETFLIX data
"From Lakehouse to GenAI: Query Apache Iceberg Data Using Amazon Bedrock and Athena" by Gunal K
#rag #amazon-bedrock #generative-ai #data-analytics #artificial-intelligence
#rag #amazon-bedrock #generative-ai #data-analytics #artificial-intelligence
From Lakehouse to GenAI: Query Apache Iceberg Data Using Amazon Bedrock and Athena
Learn how to build a governed natural-language query layer on top of an Apache Iceberg lakehouse using Amazon Bedrock, AWS Glue, Amazon Athena, and RAG. This architecture combines semantic metadata, NL2SQL, SQL validation, and data governance to let users interact with enterprise data using natural language while keeping the underlying data platform secure and auditable.
community.aws
September 23, 2026 at 7:30 AM
"From Lakehouse to GenAI: Query Apache Iceberg Data Using Amazon Bedrock and Athena" by Gunal K
#rag #amazon-bedrock #generative-ai #data-analytics #artificial-intelligence
#rag #amazon-bedrock #generative-ai #data-analytics #artificial-intelligence
NL2SQL: Oracle AI Database の COMMENT・ANNOTATIONS・Domainの違いを整理して Select AIで動作検証してみてみた
#AIReadyData #SelectAI #annotation #oracle #AI
#AIReadyData #SelectAI #annotation #oracle #AI
NL2SQL: Oracle AI Database の COMMENT・ANNOTATIONS・Domainの違いを整理して Select AIで動作検証してみてみた - Qiita
本記事は、Select AIとAI Ready Dataを扱う2部構成の第2部です。 第1部：Select AIがMetadataからSQLを生成する仕組みとAI Ready Dataを整理してみてみた 第2部：COMMENT・ANNOTATIONS・Domainの...
qiita.com
September 15, 2026 at 11:20 PM
NL2SQL: Oracle AI Database の COMMENT・ANNOTATIONS・Domainの違いを整理して Select AIで動作検証してみてみた
#AIReadyData #SelectAI #annotation #oracle #AI
#AIReadyData #SelectAI #annotation #oracle #AI
