PromptQL addresses the limitations of traditional approaches like RAG, Text-to-SQL, and Tool Calling. PromptQL provides measurable improvements in accuracy (with a clear path to reaching 100% accuracy) when connecting LLMs to enterprise data.

In today's data-driven world, connecting AI to your organization's data remains a significant challenge. What if you could build AI assistants that can query databases, access customer support tickets, and even take actions—all using natural language?

By integrating web search in PromptQL, you can enrich your private data – stored in any systems like Snowflake, BigQuery, PostgreSQL, MongoDB, or accessed through APIs – with current real-time details from the web.

One of the most effective guardrails in AI systems today is human-in-the-loop (HITL). This blog explores why HITL systems are crucial, how they align with the principles of agentic AI, and how PromptQL simplifies their implementation in the AI Assistant interface.

Over the past few weeks, we’ve introduced a series of exciting updates to PromptQL that enhance its power, accessibility, and collaboration capabilities. These enhancements are focused on simplifying workflows. Everything is now live and ready to explore!

With the PromptQL Program API, you can now trigger PromptQL programs via HTTP, bringing flexibility and intelligence to your business workflows. The API ensures seamless integration outside the chat interface of the PromptQL Playground.