Having talked to multiple folks who are β€˜building AI’, I’ve seen firsthand how easy it is to get swept up in the latest buzzwords. One such buzzword is 𝘝𝘦𝘀𝘡𝘰𝘳 𝘚𝘡𝘰𝘳𝘦𝘴. Let’s dissect some prevailing beliefs about vector stores:

1. Vector stores as the default

  • π‘΄π’šπ’•π’‰: Vector stores are the default for all AI retrieval.
  • π‘Ήπ’†π’‚π’π’Šπ’•π’š: While vector stores are powerful, they aren’t always the best fit. For instance, if you’re dealing with a database of Customer Events, a traditional query can efficiently fetch the data, which can then be processed by a language model for further insights.

2. The new era of application development

  • π‘΄π’šπ’•π’‰: Vector stores signal a new era in application development.
  • π‘Ήπ’†π’‚π’π’Šπ’•π’š: The underlying tech, like LLMs and embeddings, is based on transformer technology. It’s an expansion, not a replacement. Think of it as adding a new tool to your workshop, not rebuilding the entire workshop.

3. The β€œAI-first” badge of honor

  • π‘΄π’šπ’•π’‰: Using vector stores = β€œAI-first” badge of honor.
  • π‘Ήπ’†π’‚π’π’Šπ’•π’š: Being β€œAI-first” is about strategy and innovation. For example, a company might use AI to enhance user experience, but that doesn’t mean every tool in their stack is AI-driven.
  • π‘΄π’šπ’•π’‰: Vector search is universally superior.
  • π‘Ήπ’†π’‚π’π’Šπ’•π’š: It depends on the data. For unstructured data, vector search shines. But if you’re searching a product catalog with specific attributes, traditional keyword search might be faster and more accurate.

I’d love to hear from you. Have you encountered other myths in the AI space? Happy to take it over DMs too.

<
Previous Post
Understanding Venture Capitalist Exit Strategies
>
Next Post
Extracting comments from DOCX files: A python solution