Jason Haley is a Full Stack Solution Architect at Jason Haley Consulting, LLC, where he provides custom Azure and .NET application development solutions for a variety of clients. With over 20 years of experience using Microsoft technologies, he has earned the title of Microsoft Azure MVP and holds numerous certifications.
His expertise lies in developing Web Applications and Single Page Applications (SPA) using Blazor, Angular, jQuery, ASP.Net Core, Entity Framework Core, Redis, SQL Server, and Windows Azure Active Directory. In addition, he customizes build processes for Azure DevOps pipelines and creates courseware for .NET and Azure topics. He is deeply passionate about learning and sharing his knowledge with the local Azure and .NET community, and he leads two user groups in the Boston area.
Topics of Discussion:
[3:40] The two things that have stuck out in Jason's career.
[5:36] When Jason started paying attention to GenAI.
[9:12] Looking at GenAI from a solution perspective.
[10:52] Where to start as a .NET developer.
[16:49] Why aren't there more examples in C#?
[18:02] What is Graph RAG?
[19:11] Using language models for natural language processing tasks, including prompt engineering and token limits.
[20:56] The importance of prompt engineering, and how to optimize prompts.
[25:04] Cost and mechanics of using OpenAI's language model in Azure.
[32:12] Using Azure AI services for business problems and thinking about AI as an intern.
[34:48] Recommendations for .NET developers to get started with Azure Open AI and semantic search.
Mentioned in this Episode:
Clear Measure Way
Architect Forum
Software Engineer Forum
Programming with Palermo — New Video Podcast! Email us at programming@palermo.net.
Clear Measure, Inc. (Sponsor)
.NET DevOps for Azure: A Developer's Guide to DevOps Architecture the Right Way, by Jeffrey Palermo — Available on Amazon!
Jeffrey Palermo's Twitter — Follow to stay informed about future events!
Jason Haley website
Generative AI for Beginners
Azure OpenAI RAG Pattern using a SQL Vector Database
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