RAG can solve several issues associated with generative AI, such as reducing hallucinations, providing up-to-date information, incorporating domain-specific knowledge, allowing easy updates, and offering source citations. However, for a RAG framework to be effective, it requires comprehensive and precise model training guided by human domain-expert annotators. The article concludes by stating that RAG can address many current limitations of generative AI and, with advanced technical processes and ethical safeguards, can make generative AI a powerful tool for positive change.
Key takeaways:
- Retrieval-augmented generation (RAG) is an AI framework that allows a generative AI model to access external information to enhance its responses, which can increase accuracy, reliability and trust in AI systems.
- RAG works in a two-step process involving retrieval and generation, where a user's query triggers a relevancy search among external documents, and the retrieved information is used to formulate a response.
- RAG can solve several problems such as reducing hallucinations in AI responses, providing up-to-date information, augmenting domain-specific knowledge, allowing easy updates, and providing source citations for verification and fact-checking.
- Training and integrating a RAG framework requires comprehensive and precise model training guided by domain experts, and it can address many of the current limitations of generative AI by increasing accuracy and transparency.