RAG in business: moving from POC to a reliable, secure, and measurable assistant
A production RAG is not judged by a few convincing answers. It must know its sources, respect rights, measure its errors, and properly refuse when evidence is lacking.
Topic
RAG
Agentic RAG or classic RAG: when to let AI plan its research?
Letting an agent rephrase, search, verify, and start over can improve complex questions. This also increases variability, cost, and the safety surface.
Preparing your data for generative AI: inventory, quality, rights, and traceability
Most of the difficulties of an AI project appear before the model: unavailable sources, outdated documents, implicit rights, and lack of an owner.
RAG, fine-tuning or prompt engineering: choosing the right method of specializing a model
RAG provides knowledge at the moment of response. Fine-tuning mainly changes the learned behavior. The prompt frames a task. Confusing them leads to costly projects that are difficult to evaluate.