Now in production: we're rolling out RAG in two scenarios — FAQ systems and search across the company's network file share. Incremental document indexing, embeddings, a vector store and a chat interface answering from the corporate knowledge base — all inside the corporate perimeter, no data leaves it.
The experiments confirmed the main thing: for "find it and explain it based on our documents", the approach works far better than classic search.
Moved from reading to experimenting: building RAG pipelines on Yandex Cloud AI Studio — vector databases, embeddings, integration with our own data sources.
The goal of these experiments is to understand how well the approach fits our internal documentation systems and knowledge bases, and where its limits are: document chunking quality, index freshness, hallucinations on weak chunks.
Got acquainted with the term and the approach: RAG (Retrieval-Augmented Generation) — before answering, the model gets relevant chunks from an external knowledge base instead of relying only on what it memorized during training.
Looks like the key to corporate scenarios where data can't leave the perimeter: the model can be small and local, while the knowledge stays fresh and your own. Digging into what such a pipeline is made of.