RAG Retrieval Accuracy: 38%. After the Fix: 87%. The Model Was Never Touched.
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That's a rebuild I shipped. The system: a RAG assistant for fraud analysts — ask it "how do we handle card testing followed by a successful auth?" and it should answer from the team's own SOPs and case history. The complaint: the answers were wrong, therefore the model must be dumb, therefore procurement should buy a bigger model. The model was fine. It was answering perfectly — from garbage context. Walk the forensic trail with me, because every step is checkable on your own system this week.…
1Key Takeaways
- The system: a RAG assistant for fraud analysts — ask it "how do we handle card testing followed by a successful auth?" and it should answer from the team's own SOPs and case history.
- The complaint: the answers were wrong, therefore the model must be dumb, therefore procurement should buy a bigger model.
- It was answering perfectly — from garbage context.
- Walk the forensic trail with me, because every step is checkable on your own system this week.….
2AIWedia Score
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3Why it matters
Coding AI shifts how fast software ships and how much human review each change needs. DEV — AI reports that the system: a RAG assistant for fraud analysts — ask it "how do we handle card testing followed by a successful auth?" and it should answer from the team's own SOPs and case history.
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