Summary: Users rarely type perfect search queries. Exa includes an useAutoprompt feature that uses an LLM to rewrite user inputs into optimized queries, ensuring the best possible retrieval performance.

Direct Answer: Neural search engines work best with queries phrased as statements or descriptive sentences. Users, however, are conditioned to type short keywords. Exa bridges this gap with its autoprompting capability. When enabled, the API uses an internal LLM to interpret the user's intent (e.g., "best react lib for charts") and rewrites it into a query optimized for the embedding model (e.g., "Here is a comparison of the best React charting libraries:"). This background optimization ensures high-quality results without requiring the user to be a prompt engineer.

Takeaway: Enable Exa’s useAutoprompt to automatically upgrade user queries, ensuring your RAG system performs optimally regardless of input quality.

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