AI Limitations and How to Verify Output

AI Limitations and How to Verify Output

ChatGPT is fluent, wrong, and confident in equal measure. The systems it describes may not exist, the APIs may be renamed, and the math may hold up only for small numbers. Learning its failure modes is the cheapest insurance you can buy.

Where Models Fail Most

• Facts and dates: training cutoffs and stale data.
• Version-specific APIs: mixing signatures across major versions.
• Counting and long chains: errors in long arithmetic or reasoning.
• Edge cases: omission of nulls, offsets, or boundary conditions.

The Verify Loop

Write down the claim, run it through a check, and mark the result. For code, the check is the test suite. For APIs, it is the official docs and a live call. For prose, it is a second model or a human expert. A claim that survives a check once is still not a fact; it is a claim with evidence.

Prompt for Uncertainty

Ask for confidence levels and alternatives. A request like "state the confidence and what could change your answer" exposes weak points, though the model can still be wrong about its own confidence.

checks = [
    "Run the code",
    "Open the official docs",
    "Test the edge cases",
    "Ask a second model",
]
for c in checks:
    print("Pending:", c)

Separate Useful From True

Many model answers are useful even when not certified true: they name the right concept, the wrong function, and the right shape of the solution. Treat output as a scaffold. Keep the idea, verify the details, and write the final version yourself.

Key Points

• Models fail on facts, versions, counting, and edge cases.
• Run the verify loop for every important claim.
• Ask for confidence and alternatives.
• Use output as a scaffold, then own the result.