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- Despite its ubiquity, RAG-enhanced AI still poses accuracy and safety . . .
Though Retrieval-Augmented Generation has been hailed — and hyped — as the answer to generative AI's hallucinations and misfires, it has some flaws of its own
- Why RAG won’t solve generative AI’s hallucination problem
But a number of generative AI vendors suggest that they can be done away with, more or less, through a technical approach called retrieval augmented generation, or RAG
- Retrieval-augmented generation - Wikipedia
Retrieval-augmented generation (RAG) enhances large language models (LLMs) by incorporating an information-retrieval mechanism that allows models to access and utilize additional data beyond their original training set
- Generative AI risk cheat sheet: training vs. RAG
On the other hand, RAG is most useful when the model needs to provide factual, updated, or nuanced information that goes beyond the training data RAG can dynamically pull data relevant to the query at hand, offering a richer and more precise output tailored to the request
- AI godfather Yoshua Bengio says current AI models are showing dangerous . . .
Yoshua Bengio is warning that current models are displaying dangerous traits as he launches a new non-profit developing “honest” AI
- Tricking AI — 2 RAG attacks - Medium
AI models, including RAG, are susceptible to manipulation through techniques like prompt injection, which tricks the model into generating misleading information Hackers can also exploit RAG’s
- Is Your AI Lying to You? The Hidden Risks of RAG and How to Fix Them . . .
RAG is often viewed as a fix for AI’s hallucination problem, but without proper governance, it can become an amplifier of misinformation rather than a solution RAG does not inherently validate the truthfulness of retrieved data If the sources it pulls from are biased, manipulated, or outdated, RAG will still confidently return misinformation
- RAG: When Your AI Needs a Cheat Sheet - sundog-education. com
Retrieval Augmented Generation (RAG) has become one of the most practical techniques in generative AI, and I like to call it ‘cheating to win ’ Think of it as an open-book exam for large language models – instead of relying solely on their training, they get to peek at external sources for answers
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