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Having explored search technologies for almost a decade, I can honestly say nothing has been as disruptive as the recent rise of Retrieval Augmented Generation (RAG). This system is revolutionising search and information retrieval using vector search with generative AI to produce direct answers based on trusted data.
In my search projects, experimenting with RAG has led me to consider its potential enhancements; I believe RAG is still too limited to meet users’ needs and needs an upgrade.
Don’t get me wrong, RAG is excellent and is absolutely a step in the right direction for information retrieval technologies. I’ve used RAG since the advent of GPT-2 in 2021, which has significantly helped boost my productivity when looking for valuable information from my own notes or work documents. RAG has many advantages:
- Vector Search Fusion: RAG introduces a novel paradigm by integrating vector search capabilities with generative models. This fusion enables the…
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