Why is "fixed-length" chunking often insufficient? How would you handle a document where a single sentence contains a critical fact but spans a chunk boundary?

If your retriever finds 50 relevant "top" chunks but your LLM context window only fits 10, how do you decide which ones to keep?

How do you build a ground-truth dataset to evaluate if your RAG system is actually improving over time?

If a company has a 1,000-page internal wiki that updates daily, would you recommend RAG or fine-tuning? Why?