Developer Survey

GenAI and search: Insights from a survey of 800+ search developers

Learn about industry trends, preferred tools, and use cases that other developers — like you — are using. Get the insights from Elasticsearch and Dimensional Research to learn more on how to solve generative AI challenges.

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Your path to AI success

  • 10
    %

    developers have deployed GenAI in production — even though nearly 90% have already identified a GenAI use case.

  • 93
    %

    agree that retrieval-augmented generation (RAG) is important for getting value out of generative AI.

  • 60
    %

    consider reliability, cost, and operational simplicity as the top vector database selection factors.

There's a clear trend in adoption and excitement around the potential for RAG and GenAI. The goal is to transform customer support with knowledge base self-service and helpful chatbots.

Nearly half of respondents see gaps in AI skills, legal complications, and data privacy as hurdles to generative AI implementation.

How Elastic fits into the puzzle

Respondents already using the Elastic Search AI platform mentioned fast emerging GenAI use cases: AI/MLOps, training purposes, vector search, and generative search for RAG applications.

Your one-stop shop for getting started

At Elastic Search Labs, you'll find everything you need to build AI into your search apps. Blogs, research, code repos, tutorials, integrations, and more.

Visit Search Labs