Retrieval-augmented systems work better for troubleshooting when they match on intermediate problem states rather than treating cases as whole documents—this simple structural change significantly improves finding relevant guidance.
RAFT improves how AI troubleshooting agents find relevant past cases by treating support tickets as multi-step journeys rather than static documents. Instead of retrieving entire cases, it matches the current problem state to intermediate steps in historical cases and returns the full trajectory from that matching point, helping agents understand what happened next in similar situations.