KTS specialist Katya Poslavskaya highlights the challenges of deploying RAG systems in corporate environments. While prototyping often yields promising results, scaling these solutions to large, noisy, and constantly evolving databases frequently leads to performance issues.
The primary obstacles include managing data across multiple sources and owners, as well as maintaining the accuracy of corporate knowledge bases like Confluence. These factors increase the risk of providing incorrect information to end users.
Based on two years of experience in building RAG systems for corporate clients, the author suggests that LLM hallucinations are rarely the fault of the model itself. Instead, success in production depends on fine-tuning processes from data chunking to final generation.
