Prompt Engineering's" Database query with Generative AI embedded to MS Teams

    Client : Analytics and digital solutions company
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    case study
    • Azure
    • IT/ITeS

    Industry: Global Service Conglomerate

    Problem Statement Problem Statement

    Organizations face a significant challenge when stakeholders, particularly those without extensive technical expertise, attempt to retrieve specific information such as attrition numbers or headcount for a particular department from the database.

    To solve this, the client was looking for a solution based out of Generative AI/LLM that can help even non-technical stakeholders to get their queries answer without being time-consuming, technical, and error prone. This would also help save valuable time of technical personnel reducing inefficiencies and disruptions.

    Challenges FacedChallenges Faced

    Due to the multifaceted nature of this process, retrieving data from a database from the end user usually meets with some challenges like:

    • Need to understand the complex database schema
    • Knowing how to join multiple tables to retrieve relevant data
    • Writing SQL queries to extract the desired information
    • Lack of time from limited technical personnel
    • Solution Areas Solution Areas

      We leveraged the natural language processing capabilities of Generative AI/LLM tools to create a tool that not only understands the prompts by the end user but also generates the right result. Some of the key feature areas of the solution are:

      • Direct integration into MS Teams making it simple and versatile in usage
      • Generated data, tables, and plots in real-time in accordance with the user query
      • Takes advantage of the better privacy and security protections included in API offerings (as compared to the end-user applications)
      Architecting with the Best Tech Stack
      • Power Automate
      • Azure OpenAI
      • Azure DevOps
      • ChatGPT
      • Microsoft Teams
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    Business Impact
    • 68% reduction in queries raised for prompts to technical staff
    • 80% felt improved accessibility
    • 43% reduction in errors associated with manual SQL writing
    Project Timeline
    • Ongoing

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