Pratibha Kumari’s Post

Unleash Natural Language Query Power with AtScale’s Latest Whitepaper! Read the Whitepaper: https://bit.ly/4hBinqV Transform Data Access with GenAI and AtScale’s Semantic Layer Insights Explore the future of data accessibility with AtScale’s new whitepaper, “Enabling Natural Language Prompting with Semantic Layer and Generative AI.” Discover how AtScale’s semantic layer empowers Large Language Models (LLMs) to deliver accurate, context-rich insights directly from your data. This resource dives into overcoming common challenges in natural language data queries and provides key strategies for building effective AI-driven data ecosystems. Don’t miss out! About the Paper: Enable Natural Language Prompting with AtScale’s Semantic Layer and Generative AI As enterprises grow their data warehouses, the bottleneck of human analysts becomes more pronounced. Text-to-SQL solutions leveraging Large Language Models (LLMs) face challenges without a source of business logic and schema interactions. This whitepaper explores integrating the AtScale Semantic Layer and Query Engine with an LLM to improve Text-to-SQL performance. Key Highlights • Enhanced Accuracy: Achieves 92.5% accuracy in translating natural language questions into SQL queries. • Simplified Query Generation: Removes the need for LLMs to generate joins or complex business logic, reducing errors and improving efficiency. • Business Context Integration: Provides LLMs with essential business metadata, ensuring consistent and accurate results. Read the Whitepaper: https://bit.ly/4hBinqV

Unleash Natural Language Query Power with AtScale’s Latest Whitepaper! Read the Whitepaper: https://bit.ly/4hBinqV Transform Data Access with GenAI and AtScale’s Semantic Layer Insights Explore the future of data accessibility with AtScale’s new whitepaper, “Enabling Natural Language Prompting with Semantic Layer and Generative AI.” Discover how AtScale’s semantic layer empowers Large Language Models (LLMs) to deliver accurate, context-rich insights directly from your data. This resource dives into overcoming common challenges in natural language data queries and provides key strategies for building effective AI-driven data ecosystems. Don’t miss out! About the Paper: Enable Natural Language Prompting with AtScale’s Semantic Layer and Generative AI As enterprises grow their data warehouses, the bottleneck of human analysts becomes more pronounced. Text-to-SQL solutions leveraging Large Language Models (LLMs) face challenges without a source of business logic and schema interactions. This whitepaper explores integrating the AtScale Semantic Layer and Query Engine with an LLM to improve Text-to-SQL performance. Key Highlights • Enhanced Accuracy: Achieves 92.5% accuracy in translating natural language questions into SQL queries. • Simplified Query Generation: Removes the need for LLMs to generate joins or complex business logic, reducing errors and improving efficiency. • Business Context Integration: Provides LLMs with essential business metadata, ensuring consistent and accurate results. Read the Whitepaper: https://bit.ly/4hBinqV

Transform Data Access with GenAI and AtScale’s Semantic Layer Insights Unleash Natural Language Query Power with AtScale’s Latest Whitepaper! Read the Whitepaper: https://bit.ly/4hBinqV Transform Data Access with GenAI and AtScale’s Semantic Layer Insights Explore the future of data accessibility with AtScale’s new whitepaper, “Enabling Natural Language Prompting with Semantic Layer and Generative AI.” Discover how AtScale’s semantic layer empowers Large Language Models (LLMs) to deliver accurate, context-rich insights directly from your data. This resource dives into overcoming common challenges in natural language data queries and provides key strategies for building effective AI-driven data ecosystems. Don’t miss out! About the Paper: Enable Natural Language Prompting with AtScale’s Semantic Layer and Generative AI As enterprises grow their data warehouses, the bottleneck of human analysts becomes more pronounced. Text-to-SQL solutions leveraging Large Language Models (LLMs) face challenges without a source of business logic and schema interactions. This whitepaper explores integrating the AtScale Semantic Layer and Query Engine with an LLM to improve Text-to-SQL performance. Key Highlights • Enhanced Accuracy: Achieves 92.5% accuracy in translating natural language questions into SQL queries. • Simplified Query Generation: Removes the need for LLMs to generate joins or complex business logic, reducing errors and improving efficiency. • Business Context Integration: Provides LLMs with essential business metadata, ensuring consistent and accurate results. Read the Whitepaper: https://bit.ly/4hBinqV

🚀 Exciting News in BI: AtScale Unveils Natural Language Query Capabilities! 🚀 In the fast-evolving world of data-driven decision-making, staying ahead of the curve is crucial. AtScale has just embarked on a journey that revolutionizes data access for business users with its new Natural Language Query (NLQ) capabilities. Why This is a Game Changer Imagine unlocking insights with a simple question like, “What are our highest-selling products over $1,000?” No SQL, no technical jargon—just plain English. With AtScale's Semantic Layer and cutting-edge Generative AI, you can now gain instant, accurate responses, eliminating bottlenecks in decision-making. Here's why AtScale’s NLQ could redefine your business intelligence strategy: 1️⃣ Empowerment Without Expertise: Say goodbye to complex data engineering. AtScale allows every team member to become a data hero, by providing answers from existing BI reports or custom queries effortlessly. 2️⃣ Unmatched Accuracy: With a 92.5% accuracy rate in text-to-SQL tasks, AtScale proves that data insights are not only rapid but also remarkably precise. 3️⃣ Democratized Data Access: By bridging AI with BI, AtScale brings insights to all corners of your organization, enhancing collaboration and innovation. 🔍 Data Insights Without Limits 🔍 The true power of AtScale lies in its integration with platforms like Snowflake, Databricks, and more. The NLQ functionality is supported by the business context and metadata within the Semantic Layer, ensuring faster and more accurate insights. 📈 Accelerate Your Business Decisions: Let’s eliminate the wait for insights and embrace a future where every decision is backed by real-time data. 💥 Ready to Transform Your Data Strategy? 💥 Dive into the future of business intelligence with AtScale’s NLQ. The era of accessible, democratized data insights is here, and it’s time to lead—not lag. 👉 Unlock the full potential of your data with AtScale’s latest whitepaper.  Download Here: https://a.7wd.at/b87m7S #AtScale #BusinessIntelligence #NaturalLanguageQuerying #AI #SemanticLayer #DataDrivenDecisions #Innovation #Leadership

Existing methods like Text-2-SQL and Retrieval-Augmented Generation (RAG) often fall short when dealing with complex natural language queries over databases, typically addressing only a limited range of questions involving relational algebra or basic point lookups. Researchers proposed a technique Table-Augmented Generation (TAG) combines the logical reasoning of databases with the natural language capabilities of language models (LMs). It involves: 1. Query Synthesis: Converting natural language requests into executable database queries. 2. Query Execution: Running the queries on the database. 3. Answer Generation: Generating answers using the results and the original query. Research highlights that TAG significantly surpasses existing methods, boasting 20-65% higher accuracy in managing intricate queries. This advancement promises improved natural language understanding and data management capabilities. Link: https://lnkd.in/gsfDWrPq Code: https://lnkd.in/gucAeYJe #AI #NaturalLanguageProcessing #RetrievalAugmentedGeneration #Text2SQL

𝐔𝐧𝐢𝐟𝐲𝐢𝐧𝐠 𝐀𝐈 𝐚𝐧𝐝 𝐃𝐚𝐭𝐚𝐛𝐚𝐬𝐞𝐬: 𝐀 𝐍𝐞𝐰 𝐄𝐫𝐚 𝐰𝐢𝐭𝐡 𝐓𝐚𝐛𝐥𝐞-𝐀𝐮𝐠𝐦𝐞𝐧𝐭𝐞𝐝 𝐆𝐞𝐧𝐞𝐫𝐚𝐭𝐢𝐨𝐧 (𝐓𝐀𝐆) I just came across this wonderful research paper which introduces a groundbreaking paradigm: 𝐓𝐚𝐛𝐥𝐞-𝐀𝐮𝐠𝐦𝐞𝐧𝐭𝐞𝐝 𝐆𝐞𝐧𝐞𝐫𝐚𝐭𝐢𝐨𝐧 (𝐓𝐀𝐆). The paper identifies a gap in existing AI systems that aim to serve natural language queries over databases. Current methods like Text2SQL and Retrieval-Augmented Generation (RAG) are limited in scope. Text2SQL focuses on converting natural language questions into SQL queries, which only works for questions expressible in relational algebra. RAG, on the other hand, handles queries that can be answered with simple point lookups. Both approaches fail to address more complex queries that require semantic reasoning and world knowledge, which are often needed in real-world applications 𝐏𝐫𝐨𝐩𝐨𝐬𝐞𝐝 𝐒𝐨𝐥𝐮𝐭𝐢𝐨𝐧 The authors propose the Table-Augmented Generation (TAG) model as a unified solution. TAG integrates the capabilities of language models (LMs) and database systems to handle a broader range of natural language queries. The TAG model consists of three main steps: 𝐐𝐮𝐞𝐫𝐲 𝐒𝐲𝐧𝐭𝐡𝐞𝐬𝐢𝐬: Translating a natural language request into an executable database query. 𝐐𝐮𝐞𝐫𝐲 𝐄𝐱𝐞𝐜𝐮𝐭𝐢𝐨𝐧: Running the generated query on the database to retrieve relevant data. 𝐀𝐧𝐬𝐰𝐞𝐫 𝐆𝐞𝐧𝐞𝐫𝐚𝐭𝐢𝐨𝐧: Using the language model to generate a natural language answer from the retrieved data and the original request. 𝐊𝐞𝐲 𝐅𝐢𝐧𝐝𝐢𝐧𝐠𝐬: 𝐓𝐀𝐆 𝐌𝐨𝐝𝐞𝐥: Combines the strengths of language models (LMs) and database systems to handle a broader range of queries.  𝐒𝐢𝐠𝐧𝐢𝐟𝐢𝐜𝐚𝐧𝐭 𝐀𝐜𝐜𝐮𝐫𝐚𝐜𝐲 𝐈𝐦𝐩𝐫𝐨𝐯𝐞𝐦𝐞𝐧𝐭𝐬: TAG implementations achieve up to 65% higher accuracy on complex queries compared to existing methods. 𝐄𝐟𝐟𝐢𝐜𝐢𝐞𝐧𝐜𝐲: TAG systems are designed to be efficient, with execution times significantly lower than traditional methods. 𝐈𝐦𝐩𝐚𝐜𝐭 𝐚𝐧𝐝 𝐒𝐢𝐠𝐧𝐢𝐟𝐢𝐜𝐚𝐧𝐜𝐞: TAG has the potential to transform how organizations utilize AI for data management. By enabling more accurate and efficient processing of complex queries, TAG can unlock new insights and drive better decision-making across industries. 𝐑𝐞𝐬𝐞𝐚𝐫𝐜𝐡 𝐏𝐚𝐩𝐞𝐫: https://lnkd.in/dArftEGM Feel free to reach out or comment below with your insights! #AI #DataManagement #Innovation #TableAugmentedGeneration #TAG #Research

LAMBDA: A Large Model Based Data Agent Large Language Models (LLMs) have been instrumental in pushing innovation across multiple domains. However, despite these advancements, the current LLM paradigm encounters challenges and limitations in data science applications, particularly in domains that demand extensive expertise and advanced coding knowledge. To address this researchers have introduced LAMBDA (A Large Model Based Data Agent), a novel open-source, code-free multi-agent data analysis system. LAMBDA is designed to address data analysis challenges in complex data-driven applications through the use of innovatively designed data agents that operate iteratively and generativity using natural language. At the core of LAMBDA are two key agent roles: the programmer and the inspector, which are engineered to work together seamlessly. Specifically, the programmer generates code based on the user’s instructions and domain-specific knowledge, enhanced by advanced models. Meanwhile, the inspector debugs the code when necessary. To ensure robustness and handle adverse scenarios, LAMBDA features a user interface that allows direct user intervention in the operational loop. Additionally, LAMBDA can flexibly integrate external models and algorithms through knowledge integration mechanisms, catering to the needs of customized data analysis. LAMBDA demonstrates superior performance on various machine learning (ML) datasets. Notable results with an accuracy of 100%, 98.07%, and 98.89% on datasets NHANES, Breast Cancer, and Wine respectively. To sum up, the main characteristics of LAMBDA are as follows: (1) Codingfree and natural language interface. (2) Integrating human intelligence and AI. (3) Reliability. (4) Automatic analysis report generation. Paper : https://lnkd.in/g4CSaKJ4 Checkout more paper review here https://lnkd.in/gaCZSrXm

#GenAI has multiple Use Cases and creates tremendous opportunity to create business value across multiple industries and functions. One area which takes precedent over all others is to generate insights from structured data. Organizations have spent billions of dollars in the last 5-7 years to prioritize data availability in the hope that business users can generate insights on their own but due to lack of or restricted functionality of the current dashboarding tools like #powerBI, #Qlik, #Tableau to generate the Insights using natural language, the adoption has been slow and painful. We at #EzInsightsAI have recognized this long back when we first built NLP driven framework to allow business to interact with the data using "#NLP #Search" without going through the cumbersome process of learning a tool. Now we bring you the same ease of interaction with your data stored in your databases using GenAI to generate powerful insights and collaborate more effectively for faster decision making with our #Text2SQL #Accelerator. Afterall, #data #crunching should really be #data #Munching. Watch out short video on #EzInsightsAI #TexttoSQL #Accelerator and register for a #Free #Trial!

Text to SQL - The GenAI enhanced technique, helping users access data by generating SQL queries with the lucidity of natural language, thereby, making it every business owner's next favorite! This article by Boston Consulting Group (BCG) delves deep into the beauty of Text to SQL 👇 https://lnkd.in/gcw8i2k5 #EzDataMunch #EzInsights #GenAI #SQL #nlp