RAG Is a Fancy, Lying Search Engine
a year ago
- #LLM
- #GenAI
- #RAG
- RAG (Retrieval-Augmented Generation) is a popular GenAI application design pattern that supplements user prompts with dynamically retrieved information to improve LLM responses.
- RAG is criticized for being unfit for high-stakes use cases in regulated industries due to its tendency to let LLMs 'speak last,' exposing users to hallucinations.
- RAG's popularity stems from its ease of implementation, strong early demos, and significant funding for RAG-based startups.
- A16Z's influential GenAI reference architecture indirectly promoted RAG, shaping early GenAI investment trends.
- RAG is seen as a 'fancy search engine' that still makes errors, reducing but not eliminating hallucinations.
- RAG is limited to unstructured data (documents), ignoring structured and semi-structured data crucial in regulated industries.
- Semantic Parsing is proposed as a safer alternative to RAG for high-stakes enterprise applications.