Knowledge Should Not Be Gated
The author argues that current RAG (Retrieval-Augmented Generation) architectures for AI are overly complex and gate knowledge behind proprietary pipelines. They propose a shift toward using human-readable markdown as a simpler, more accessible standard for AI knowledge bases.
Why it matters
This perspective challenges the prevailing 'vector database' paradigm in AI development, potentially influencing how companies structure their internal data for LLMs.
For most of the last few years, giving an AI system knowledge meant building infrastructure.
This is an opinion piece on technical architecture, reflecting a specific viewpoint within the software engineering community.
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