Welcome to the Coveo Passage Retrieval API demo. I'm gonna show you how a user query triggers the Passage Retrieval API and flows through to enhance the responses of different large language models. The Passage Retrieval API is the perfect solution for organizations considering building their own AI applications or leveraging AI agents because it takes care of the most complex, resource intensive, and risky part, retrieving accurate information to feed the LLM or AI agent. I'm gonna show you how easy it is to use this API to enhance the accuracy, relevance, and security of any generative AI experience, reducing hallucinations, protecting sensitive data, and significantly accelerating time to value. Right now you can see in the configuration window that we're using an existing query pipeline in our demo AI, Barca Group, which is a boating equipment manufacturer selling direct to consumers via retail locations and online. Let's start with a common scenario, entering a query like boat battery problem. The API instantly retrieves relevant text chunks, not full documents, along with source links and relevance scores, so your AI applications have reliable, current, and citable information for generated responses. Next, we see that these chunks are fed into a large language model AI ChatGPT or Gemini using a custom prompt, which for this use case would be managed by you, the customer. For instance, you might want a standard customer service tone in one application and a playful sassy pirate tone in another. Here in our testing tool, we are comparing multiple LLMs side by side to see how each one interprets the same source information. With the Passage Retrieval API, Coveo takes care of the complex retrieval of knowledge and you, the customer, can choose the LLM and its settings as well as tune the prompts to deliver exactly the kind of answers you want for your specific use case and audience. This is what makes passage retrieval so powerful. You have full control over the UI, prompt engineering, LLM choice, and its parameters. And we at Coveo make the indexing and retrieval easy and precise, especially for those who already have a Coveo index already implemented. You can create new query pipelines or use existing ones and still have the ability to fine tune them by applying weightings and business rules or leveraging machine learning like automatic relevance tuning. If your organization has unique compliance or security rules, those constraints are already handled by the Coveo search pipeline before anything is sent to the LLM. You could add any sort of metadata into the context of the query, from user access permissions to their role, country, and language, or even interaction data to signal intent. Now, the studio environment I showed you is just a testing tool we've developed internally as a way to show how the API works. But in a real world scenario, you'd of course leverage the API in your own applications and testing tools. By building Agentic apps using Coveo, you tap into the power of the Coveo AI Relevance platform, leveraging our connectors to third party data sources, hybrid ranking, security features, and decades of building enterprise grade search and retrieval and machine learning models. By delivering precise, trustworthy text to LLMs or AI agents like Salesforce Agent Force, Microsoft Copilot, Amazon Q, and SAP JUUL, you will boost accuracy, minimize hallucinations, and meet enterprise security requirements, all while improving deployment speed and reducing costs. Plus, with a unified index and multiple custom and managed solutions available to leverage it, you can scale multiple generative AI applications consistently using a single reliable retrieval method. Thanks for watching. I hope this gave you a clear idea of how the Passage Retrieval API can power fast, accurate, and highly customizable AI driven experiences tailored to your brand, your content, and your users.