Armchair Architects: LLMs & Vector Databases (Part 2)

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In this episode of the #AzureEnablementShow, Uli, Eric (@mougue) and David continue their discussion of vector databases and LLMS, including when to use prompt engineering, and the importance of fine-tuning your data. Uli suggests that there are two things that LLMs aren’t good at, then offers tips on workarounds. The conversation wraps up with a discussion of some of the pros and cons of vectorization. This is part two of a two-part series. Resources • Submit a training job in Studio https://learn.microsoft.com/en-us/azure/machine-learning/how-to-train-with-ui?view=azureml-api-2 • Artificial intelligence (AI) architecture design https://learn.microsoft.com/en-us/azure/architecture/ai-ml/ • Prepare for AI engineering https://learn.microsoft.com/en-us/training/paths/prepare-for-ai-engineering/ • Fundamentals of Generative AI https://learn.microsoft.com/en-us/training/modules/fundamentals-generative-ai/ • What's new in Azure AI Search https://learn.microsoft.com/en-us/azure/search/whats-new Related episodes • Armchair Architects: LLMs & Vector Databases (Part 1) https://aka.ms/azenable/141 • Watch more episodes in the Armchair Architects Series https://aka.ms/azenable/ArmchairArchitects • Watch more episodes in the Well-Architected Series https://aka.ms/azenable/yt/wa-playlist Chapters 0:00 Introduction 0:15 Recap on Embedding 0:39 Consider prompt engineering first 2:06 Fine tune your data 2:58 Help with hallucinations 4:49 LLMs and math 4:59 LLMs and structured d

Published 2024-01-02 · Watch on YouTube ↗
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