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Get a clear explanation of RAG, its benefits, and how it combines retrieval and generation for smarter AI responses.

Agentic Search-Augmented Factuality Evaluator (SAFE) For LLMs This study demonstrates that an LLM agent can outperform crowdsourced human annotators…

FaaF: Facts As A Function For Evaluating RAG There has been instances where another Language Model is used to vet the RAG output, which fails to detect incorrect and incomplete generated data.

Disambiguation: Using Dynamic Context In Crafting Effective RAG Question Suggestions This approach reminds of a technique initially implemented by IBM Watson called disambiguation. Where ambiguous input from the user is responded to with about five or less options to choose from. Hence allowing the user to perform disambiguation themselves, and allowing the system to learn from it.

FIT-RAG: Are RAG Architectures Settling On A Standardised Approach? As RAG is being used, vulnerabilities are emerging & solutions to these problems are starting to look very much alike.

Challenges In Adopting Retrieval-Augmented Generation Solutions I have thoroughly examined academic papers on RAG (Retrieval-Augmented Generation) and have identified several common challenges raised in studies associated with implementing retrieval-augmented solutions.