Copilot Studio: Build Intelligent Search

Key insights

• Copilot Studio: explains how to enable built-in search for your bot so users get fast, relevant answers.
  You turn on search in the studio, pick data sources, and the bot uses natural language to find results.
• Data connectors and data sources: connect Dataverse, SharePoint, SQL, Teams files, and other sources to index content.
  Choose the sources you want the bot to search and grant appropriate access.
• Embeddings and vector search: the studio creates embeddings to enable semantic search and similarity matching.
  Vector search helps the bot find conceptually related content, not just exact keyword matches.
• Relevance tuning and retrieval: configure filters, ranking, and prompt templates so retrieved content fits user intent.
  Use retrieval-augmented generation (RAG) to combine retrieved facts with the bot’s responses.
• Security and governance: enforce role-based access, data loss prevention, and compliance settings for indexed content.
  Limit who can search sensitive sources and monitor access to protected data.
• Test, monitor, and publish: preview interactions, review analytics, and iterate on prompts and indexes before publishing.
  Track usage and feedback to improve search accuracy and user experience over time.

Keywords

Copilot Studio bot intelligent search, Power Platform Shorts Copilot search, enable intelligent search Copilot Studio, semantic search Power Platform, Azure Cognitive Search Copilot integration, AI-powered bot search, Copilot Studio knowledge mining, conversational search in Copilot