Your team runs a RAG solution over a corpus of long technical manuals. During evaluation, users report that answers are frequently cut off mid-explanation and the retrieved passages often lack the surrounding context needed to answer procedural questions. Retrieval recall metrics show relevant documents are being found, but groundedness scores are low. You are currently using a fixed chunk size of 256 tokens with no overlap. Which change to your chunking strategy is MOST likely to improve answer completeness and groundedness?