Tag: RAG

Here’s how retrieval-based grounding works to reduce hallucinations and generate answers with better context.

Learn the difference between fine-tuning a model vs. using RAG and how to evaluate which is right for your use case.

Agentic RAG can help you build a company knowledge assistant so you can use actual data from your company. Here’s a practical guide for how to get started.

ICYMI: Eudald Camprubí on retrieval-augmented generation, small language models and secure AI adoption.

This blogs argues that the success of a retrieval-augmented generation (RAG) system depends more on data quality, metadata and governance than on model tuning or pipeline optimization. Without clear metadata, document ownership, permissions and freshness controls, AI systems can retrieve outdated or incorrect information, leading to hallucinations. Ultimately, trustworthy AI requires well-structured, governed data, not just more advanced models.