# text2sql A technique that translates natural-language questions into SQL queries for relational databases. This is one page of public article previews, not the complete archive. Follow Next page to continue. Summaries are not the original full articles. ## Reducing Text2SQL latency with parameterized query templates DevFeed: [Reducing Text2SQL latency with parameterized query templates]() Original publisher: [Read original article]() Author: Yury Brukau Published: 2026-08-13T00:40:32Z Content type: article Language: en Sources: [AWS Architecture Blog]() Topics: [text2sql](), [Language models]() Tags: [ai](), [amazon-bedrock](), [architecture](), [aws-lambda](), [database](), [intermediate-200](), [latency](), [llm](), [performance](), [production](), [sql](), [technical-how-to](), [text2sql]() ### AI overview The article describes using parameterized SQL query templates as a semantic caching layer for a production Text2SQL system. It reports lower latency and token consumption by matching similar questions to templates and avoiding some LLM calls. ### Source excerpt Learn how parameterized query templates reduced Text2SQL latency by 80% and cut token consumption by over 50%. This post covers the architecture behind an intelligent caching layer that uses semantic similarity to match user questions to SQL templates, bypassing expensive LLM calls. ## Institutional knowledge doesn't scale: Building an agentic data analyst DevFeed: [Institutional knowledge doesn't scale: Building an agentic data analyst]() Original publisher: [Read original article]() Author: Navo Das Published: 2026-08-03T10:45:52Z Content type: article Language: en Sources: [The incident.io Blog]() Topics: [data](), [semantic-layer](), [Large Language Model](), [text2sql](), [SQL](), [Databases](), [metric-standardization]() Tags: [agentic](), [analytics](), [building](), [data](), [incident](), [incident-channel](), [incident-management](), [incident-response](), [llm](), [outage](), [post-mortem](), [semantic-layer](), [slack-incident](), [sql](), [tool]() ### AI overview The article explains why dashboard-based self-service analytics and direct LLM access to a data warehouse leave important gaps. It describes building an agentic data analyst, called the "data brain," to distribute institutional data knowledge and help employees ask questions while addressing issues such as canonical joins, filters, metrics, and judgment required to produce correct SQL. ### Source excerpt Institutional knowledge was always the bottleneck. Here's how we built an agentic data analyst to distribute it more efficiently -- and what happened when we let the whole company ask it questions. ## Building Reliable Agentic AI Systems DevFeed: [Building Reliable Agentic AI Systems]() Original publisher: [Read original article]() Author: Martin Fowler (martin@martinfowler.com) Published: 2026-06-16T12:11:00Z Content type: article Language: en Sources: [Martin Fowler]() Topics: [Artificial Intelligence](), [Retrieval Augmented Generation (RAG)](), [text2sql](), [AI search](), [Generative AI](), [AI-generated research reports](), [data](), [Orchestration](), [observability](), [Prompt Engineering]() Tags: [agentic-ai](), [ai](), [building](), [case-study](), [data](), [drug-discovery](), [efficiency](), [engineering](), [generative-ai](), [information-retrieval](), [language-models](), [llms](), [orchestration](), [production](), [rag](), [research](), [retrieval](), [safety](), [sql](), [technical]() ### AI overview This case study describes PRINCE, a cloud-hosted platform developed by Bayer AG with Thoughtworks for pharmaceutical research. It combines Agentic Retrieval-Augmented Generation and Text-to-SQL to help researchers query decades of safety study reports, answer complex questions, and draft regulatory documents. The article focuses on context engineering, orchestration, recovery, observability, transparency, explainability, human oversight, governance, and compliance in production-ready agentic AI systems. ### Source excerpt One of the most interesting projects my colleagues have done with LLMs has been building a system with Bayer to allow pharmaceutical researchers to query decades of information about studies buried in PDF reports. Sarang Sanjay Kulkarni describes its evolution from keyword-based search to an intelligent research assistant capable of answering complex questions and drafting regulatory documents. more... ## Trino Summit 2024 resources DevFeed: [Trino Summit 2024 resources]() Original publisher: [Read original article]() Author: Manfred Moser, Monica Miller, Anna Schibli Published: 2024-12-18T00:00:00Z Content type: article Language: en Sources: [Trino Blog]() Topics: [data](), [data analytics](), [interoperability](), [text2sql](), [Kubernetes](), [Resilience](), [Security](), [Persistence](), [Artificial Intelligence]() Tags: [analytics](), [community](), [data](), [interoperability](), [kubernetes](), [resilience](), [resources](), [security](), [slack](), [software-engineer](), [summit]() ### AI overview A recap of Trino Summit 2024 sessions covering large-scale data warehouses, data lakes and lakehouses, self-service analytics, text-to-SQL, interoperability, cost efficiency, observability, policy enforcement, and Trino on Kubernetes. ### Source excerpt What a view we had at the summit! Over 700 live attendees enjoyed the sessions and learned more about Trino-related use cases and projects. Now it is time for the additional 1000 registrants, our 13000+ Trino users on Slack, and everyone else in the Trino community and beyond to enjoy the presentations and recordings at their leisure. ## The glorious lineup for Trino Summit 2024 DevFeed: [The glorious lineup for Trino Summit 2024]() Original publisher: [Read original article]() Author: Manfred Moser, Monica Miller, Anna Schibli Published: 2024-11-22T00:00:00Z Content type: news Language: en Sources: [Trino Blog]() Topics: [SQL](), [Artificial Intelligence](), [Kubernetes](), [Apache Iceberg](), [data analytics](), [text2sql](), [observability](), [Security](), [Resilience]() Tags: [2025](), [ai](), [data-analytics](), [event](), [free](), [kubernetes](), [observability](), [resilience](), [security](), [sql](), [summit]() ### AI overview The article announces the full lineup for the free, virtual, two-day Trino Summit 2024. It highlights a keynote on Trino project and community developments, a panel on AI and Trino, and sessions covering Kubernetes, observability, data lakes with Iceberg, policy enforcement, and text-to-SQL analytics. ### Source excerpt We just wrapped up our mini training series SQL basecamps before Trino Summit, and now Trino Summit 2024 is less than three busy weeks away. It's a good thing that we have also been working hard on all the preparations for the summit. Everything is coming together, and we are excited to share the full lineup for the free, virtual, two day event today. ## Text2SQL using Hugging Face Dataset Viewer API and Motherduck DuckDB-NSQL-7B DevFeed: [Text2SQL using Hugging Face Dataset Viewer API and Motherduck DuckDB-NSQL-7B]() Original publisher: [Read original article]() Author: Andrea Soria; Till Döhmen; Sen Wu; Laurel Orr; Vishal Published: 2024-04-04T00:00:00Z Content type: tutorial Language: en Sources: [Hugging Face - Blog]() Topics: [text2sql](), [DuckDB](), [hugging face](), [Large Language Model](), [SQL](), [API](), [parquet](), [data](), [Data analysis](), [Databases]() Tags: [ai](), [api](), [artificial-intelligence](), [datasets](), [duckdb](), [guide](), [hugging-face](), [llm](), [parquet](), [sql](), [text2sql]() ### AI overview This tutorial explains how to use the DuckDB-NSQL-7B large language model with the Hugging Face Dataset Viewer API, parquet files, and DuckDB to perform text2sql tasks. The model translates plain-language data requests into valid DuckDB SQL statements for data exploration and analysis. ### Source excerpt We're on a journey to advance and democratize artificial intelligence through open source and open science.