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Fault Tolerance and GPU Failures at Scale
Technical
Fundamentals GUIDE
AI Failure Modes
An AI failure mode is a repeatable way a system can produce an unacceptable result.
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Overview
Examples include unsupported claims, missed cases, data leakage, unsafe tool actions, and failures under changed inputs. Classifying failure modes helps teams test and address causes rather than treating every mistake as the same problem.
Key takeaways
- Describe triggers and consequences precisely.
- Separate model, data, and workflow failures.
- Match mitigations to the observed cause.
Deep Dive
Begin with the intended behavior and its boundaries. A wrong category, an invented citation, and a duplicated payment request require different responses. Record the trigger, observed result, affected component, and practical consequence for each failure.
Distinguish model errors from system errors. A model may correctly interpret a request while a tool executes with the wrong account, a stale document supplies outdated policy, or a retry repeats a completed operation. End-to-end verification is essential when an output can change external state.
Test ordinary variability and deliberate misuse separately. Formatting changes, dialects, missing data, long documents, and conflicting instructions can expose weaknesses without an adversary. Security tests add cases where an attacker tries to redirect behavior or access information.
Choose controls matched to the cause: input contracts, evidence checks, permission limits, transaction identifiers, abstention, or human review. Keep failed cases for regression testing and record residual uncertainty. A mitigation that catches one example should not be described as eliminating an entire class of failures.
04Worked example
Separate execution from a success claim
Imagine an assistant saying that a file was saved after its storage tool timed out.
Inspect the destination to determine whether a file exists and whether its contents match the request.
If the outcome is unknown, report that state and use a safe reconciliation step before retrying. Add the timeout case to the regression suite.
What it shows
This hypothetical example tests observable completion rather than the assistant’s description of it.
Real-World Implementation
Test that retries do not repeat an already completed action.
Check whether a summarizer preserves negation and uncertainty.
Putting it into practice
Why it matters
- Clearer decisions. It helps you separate clear technical claims from marketing language.
- Cost and budget. You can ask better implementation questions before spending money or time.
- Team and workflow. Teams with shared understanding make better product, policy, and learning decisions.
Watch out for
- Different teams may use the same term differently, so define scope early.
- Benchmarks can look strong while real-world performance is uneven.
- Ignoring data quality and evaluation plans often creates fragile outcomes.
Next steps
- Start with a plain-language definition of the outcome you need.
- Pick one success metric and one failure condition before testing.
- Run a small pilot with representative data, not a polished demo set.
- Document where AI Failure Modes helps and where simpler methods are better.
Sources and further reading
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Frequently asked questions
Does fixing one failed example prove the failure mode is eliminated?
No. Test meaningful variations and the underlying cause. A single successful replay is limited evidence.
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