Best practices: Building Enterprise Applications that leverage GenAI
How do you build enterprise AI apps that use your private data without expensive retraining?
#1about 6 minutes
Demonstrating the future of software with natural language
A live demo shows how Semantic Kernel enables a multi-lingual, chat-based interface to control devices like lights, illustrating the concept of "Software V2".
#2about 3 minutes
Building a natural language interface for PowerShell
An application translates plain English queries into PowerShell commands to retrieve system information like running processes and IP addresses.
#3about 2 minutes
Understanding the role of embeddings and vector databases
Embeddings provide a semantic, multi-dimensional representation of text tokens, which are stored and queried in vector databases to find semantic similarity.
#4about 2 minutes
Exploring native vector search in SQL Server 2025
A preview of SQL Server 2025 shows the new native vector data type and distance function, but also highlights potential linear performance scaling issues.
#5about 7 minutes
Using RAG to extend LLM knowledge without retraining
A C# code walkthrough demonstrates how Retrieval-Augmented Generation (RAG) injects new information into a model's context to override its base knowledge.
#6about 4 minutes
Implementing function calling to connect LLMs to tools
Learn how function calling enables an LLM to execute external code by mapping natural language prompts to specific functions and their arguments.
#7about 1 minute
Key takeaways for building enterprise GenAI applications
A summary emphasizes that mastering embeddings, vector databases, and function calling is essential for solving real-world problems with GenAI.
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