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AI agents often hallucinate, generating misleading responses when they lack accurate grounding. The solution? Embedding-based classification with vector similarity - ensuring agents first classify queries correctly before retrieving trusted data.

Join this live session to learn how embedding models like text-embedding-ada-002 leverage semantic similarity and cosine similarity to improve AI precision, reduce errors, and scale effortlessly in .NET/C# applications.

We’ll cover real-world industry challenges, why pre-trained embeddings outperform custom models, how to deploy them in Azure AI Foundry, and best practices for storing and managing embeddings efficiently. Why This Matters Without proper classification, AI agents can hallucinate, pulling in irrelevant or incorrect data and making unreliable predictions. Vector-based embeddings solve this by capturing the semantic meaning of queries and mapping them to the right categories, ensuring agents retrieve accurate, contextually relevant information based on cosine similarity rather than generating misleading responses.

What You’ll Learn:
- How embeddings and cosine similarity prevent AI hallucination and improve classification.
- Why pre-trained models like text-embedding-ada-002 are better than training your own Deploying and integrating embeddings with Azure AI Foundry.
- Best practices for managing embedding vectors and semantic similarity in AI-driven applications Who Should Attend.
- .NET/C# developers building AI-powered apps - Engineers working on LLM-based agents, AI search, or automation.
- Architects designing scalable AI solutions with semantic and vector-based models.
- Professionals looking to enhance AI precision and scalability.

Don’t miss this chance to level up your AI skills and make your agents smarter, faster, and more reliable using vector embeddings and semantic similarity!