Tag: Data & AI
RAG vs Fine-Tuning: When to Use Which?
RAG changes what a model knows by retrieving from a live knowledge base, while fine-tuning changes how a model behaves by training it on curated examples. In this post, we compare them in terms of accuracy, cost and citations, and show when to reach for each.
What Is Named Entity Recognition (NER) in RAG?
Named Entity Recognition (NER) finds the people, organizations and dates inside unstructured text and stores them as metadata alongside the embeddings. In this post, we explain how that metadata sharpens retrieval and show how Progress Agentic RAG automatically detects entities during ingestion.
What is a Knowledge Graph in Enterprise AI?
Knowledge graphs make relationships explicit and queryable, so enterprise AI can follow business facts instead of guessing from similar text.
What is AI Grounding and Why Does it Prevent Hallucinations?
AI grounding ties answers to verified sources, helping architects reduce hallucinations without locking retrieval into one vendor.
What is Context Relevance in RAG?
Context Relevance tells you whether retrieval handed the model the right chunks, so you can catch a bad answer at its source instead of blaming the prompt.
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