Topic
RAG and Knowledge AI
Retrieval-augmented generation is how most enterprises get an AI system that answers from their own documents without retraining a model. The model supplies the language, your data supplies the facts, and every claim can carry a citation back to its source.
The quality of a RAG system is decided less by the model and more by the parts around it: how documents are chunked, whether search is hybrid, whether results are reranked, and how rigorously the answers are evaluated for groundedness rather than fluency. These articles cover what actually moved the numbers.
Everything we have written on this
RAG Evaluation Metrics That Matter
RAGAS scores were a good starting point in 2023. The teams that avoided painful incidents added domain-specific metrics for faithfulness, context precision, and hallucination rate.
RAG Patterns for Legal, Banking, and SaaS
Between 2022 and 2024 we implemented RAG systems in legal, banking, and B2B SaaS. The architectures looked different, but the success patterns were surprisingly consistent.
Proof in production
Key terms
The service behind this
RAG Systems & Knowledge AI
AI that answers from your data, with citations, not guesses.
