Forget RAG, the Future is RAG-Fusion
Having built search products 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.
However, as a product manager who has been recently putting RAG products into a production environment, 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 generation of richer, more context-aware outputs from large language models (LLMs).
Reduced Hallucination: RAG significantly diminishes the LLM’s propensity for hallucination, making the generated text more grounded in data.
Personal and Professional Utility: From personal applications like sifting through notes to more professional integrations, RAG showcases versatility in enhancing productivity and content quality while being based on a trustworthy data source.
However, I’m finding more and more limitations of RAG:
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