FG-0606 Output Verification
Description
Systematic methods for detecting inaccurate, fabricated, or misleading content in AI-generated output. RLHF-tuned language models tend to be overconfident, their expressed confidence poorly calibrated against actual accuracy: they produce fluent, assured output regardless of whether it is correct. Hallucinated facts, invented citations, plausible-sounding but incorrect technical claims, and subtly wrong data are indistinguishable from correct output without explicit verification. Output verification is the minimum standard for using AI-generated content in any context where accuracy matters.
Implementation
Implement verification at three levels: structural (output matches requested format and constraints), factual (claims are verifiable against authoritative sources), and logical (conclusions follow from premises). Use cross-agent verification, in which a second model independently checks the output against source material, an application of the LLM-as-a-judge pattern to groundedness rather than preference scoring. Establish a verification threshold (what percentage of claims must be verified before acceptance) and adjust it based on the consequence of errors.
Success Indicators
- Factual errors are caught before output reaches production or publication
- Verification coverage is proportional to the consequence of inaccuracy
- Cross-agent verification catches errors that self-review misses
- The team maintains a running log of error types for pattern detection
Failure Modes
- Verification theater: reviewing output without actually checking claims against sources
- Over-trust in confident language: agents that express certainty are no more likely to be correct
- Single-point verification: one agent checking another without independent source access
- Verification fatigue: skipping checks on routine output that later proves to contain errors