Elastic announced Playground, a low-code interface that enables developers to build RAG applications using Elasticsearch in minutes.

While prototyping conversational search, the ability to rapidly iterate on and experiment with key components of a RAG workflow (for example: hybrid search, or adding reranking) are important— to get  accurate and hallucination-free responses from LLMs.

Elasticsearch vector database and the Search AI platform provides developers with a wide range of capabilities such as comprehensive hybrid search, and to use innovation from a growing list of LLM providers. Our approach in our playground experience allows you to use the power of those features, without added complexity.

Playground’s intuitive interface allows you to A/B test different LLMs from model providers (like OpenAI and Anthropic) and refine your retrieval mechanism, to ground answers with your own data indexed into one or more Elasticsearch indices. The playground experience can leverage transformer models directly in Elasticsearch, but is also amplified with the Elasticsearch Open Inference API which integrates with a growing list of inference providers including Cohere and Azure AI Studio.