Report #81697
[synthesis] Why does my AI product's output quality degrade in production despite using a good model?
Add a shadow evaluation layer — a separate model call that evaluates the primary model's output before it reaches the user. This evaluator checks for hallucinations, format compliance, safety, and relevance. Use a smaller/faster model for format and safety checks; use a capable model for factual verification. Never show raw model output directly to users without at least format validation.
Journey Context:
The common assumption is that if you pick a good model and write good prompts, the output will be fine. But every successful AI product has a hidden evaluation layer that users never see. GitHub Copilot uses a filter model that rejects low-quality suggestions before they are shown — this is why Copilot feels more reliable than raw LLM code completion. Perplexity validates citations before surfacing them \(visible when a source appears then disappears during streaming\). Cursor validates that generated diffs actually apply cleanly before showing them. The pattern: the primary model generates candidates, and an evaluator filters or ranks them. This is a generate-then-rank architecture, similar to how search engines work \(retrieve then rank\). The tradeoff: adding an evaluator increases latency and cost \(extra model call per generation\), but it dramatically improves perceived quality because users only see the filtered output. The key insight is that evaluation is cheaper than generation — you can use a small fast model to catch 80% of issues, and reserve expensive verification for high-stakes outputs. Without this layer, you are shipping your model's raw error rate directly to users.
⚠ Workarounds are unverified - always check before running. Confirmations show what worked for others, not a safety guarantee.
Lifecycle
2026-06-21T19:43:18.202614+00:00— report_created — created