7 techniques for making AI verify its own outputs, from self-consistency to TDD loops
goyalshaliniuk · x · 2026-09-22
Generating an answer and knowing it's correct are two different problems. Shalini Goyal lays out 7 techniques for AI output verification:
- Self-consistency checks: run the same task multiple times and compare conclusions; agreement across reasoning paths raises confidence.
- Retrieval-based verification: search documents/knowledge bases and check claims against evidence instead of trusting model memory.
- Tool-based verification: calculators for math, code execution for programming, search for current info, database queries for structured data.
- Critic models: a second model reviews output for factual errors, missing info, and unsupported claims (generate → critique → revise).
- Rule-based validation: deterministic checks on required fields, types, allowed values, business constraints, formatting.
- Test-driven verification: generate code → run tests → inspect failures → fix → retest.
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