Rag 2026: Evolution from a Feature to an Engineering Discipline In 2023, RAG focused on rescuing weak LLMs. Despite advancements with models having longer contexts, research indicates a decline in quality, emphasizing the continued relevance of RAG. Andrey Sokolov from Yandex R&D provided insights on utilizing RAG in 2026, citing cases such as the Alice project. By segregating internal and external knowledge and using RAG solely for factual queries, the team achieved a -23% context and +3% quality. The key takeaway is the importance of quality assessment tools in maximizing RAG's potential and its direct impact on model knowledge for transitioning to more intelligent models. The evolution of RAG into a comprehensive engineering discipline by 2026 is evident through metrics, training methods, and architectural frameworks. Different projects like Alice and Neurosupport showcase the diverse applications and challenges, emphasizing the ongoing need for advancements in the field.

In 2023, RAG focused on rescuing weak LLMs. Despite advancements with models having longer contexts, research indicates a decline in quality, emphasizing the continued relevance of RAG.

Andrey Sokolov from Yandex R&D provided insights on utilizing RAG in 2026, citing cases such as the Alice project. By segregating internal and external knowledge and using RAG solely for factual queries, the team achieved a -23% context and +3% quality. The key takeaway is the importance of quality assessment tools in maximizing RAG's potential and its direct impact on model knowledge for transitioning to more intelligent models.

The evolution of RAG into a comprehensive engineering discipline by 2026 is evident through metrics, training methods, and architectural frameworks. Different projects like Alice and Neurosupport showcase the diverse applications and challenges, emphasizing the ongoing need for advancements in the field.