While large language models (LLMs) hold immense promise for building AI applications and agentic systems, ensuring they generate reliable and trustworthy outputs remains a persistent challenge. Effective data management — particularly how data is stored, retrieved and accessed — is crucial to overcoming this issue. Retrieval-augmented generation (RAG) has emerged as a widely adopted strategy, grounding LLMs in external knowledge beyond their original training data.
The standard, or baseline, implementation of RAG typically relies on a…
