Mastering Semantic Classification with Embeddings and Vector Similarity in .NET/C#

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GenAI 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. Why This Matters

Without proper classification, GenAI 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.
Ensures 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, improve classification, and ensure accurate, contextually relevant responses.
Why pre-trained models like Text-Embedding-Ada-002 outperform custom models, and how to deploy them in -Azure AI Foundry with hands-on coding in .NET/C#.
Best practices for managing Embedding Vectors and Semantic Similarity in GenAI-Driven Applications for scalability and precision.

Who Should Attend

.NET/C# Developers building AI-powered apps and solutions.
Engineers working on LLM/GenAI-based Agents, AI search, or automation.
Architects designing scalable AI solutions using Semantic and Vector-Based Models, and professionals aiming 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! 00:08 Code of conduct 00:58 Speaker intro 40:58 Questions [eventID:25277]