Quick Answer
Yes. Exact error messages are among the most useful pieces of evidence we can give AI during debugging.
We repeatedly found that a concrete failure produces better troubleshooting than “it doesn't work.”
An error can identify a file, line, function, exception type, unexpected value or failed operation.
But the error location is not always the original cause, so the best debugging package also includes the relevant code, expected behavior and what actually happened.
Why “It Doesn't Work” Is Weak Evidence
Without a concrete failure, AI has to generate possibilities.
Maybe the syntax is wrong.
Maybe a dependency is missing.
Maybe the input is unexpected.
Maybe the path is wrong.
Maybe a plugin conflicts.
That can produce a long list of plausible troubleshooting steps without narrowing the real problem very much.
An Error Message Creates Anchors
A real error changes the task.
Now there may be:
a filename;
a line number;
an exception class;
a function name;
a stack trace;
an unexpected type;
or a failed resource.
Those details sharply reduce the number of explanations that deserve attention.
Do Not Paraphrase Away the Useful Part
We learned to give AI the exact error when possible.
A human summary such as “WordPress says the plugin is broken” can remove the technical detail that makes the message useful.
The exact text may contain the function, path or exception that points toward the cause.
For debugging, precision is usually more valuable than a cleaner description.
The Four Things We Try to Provide
Our best troubleshooting inputs contain:
the exact error;
the relevant code;
what we expected;
and what actually happened.
If the environment matters, we add that too.
Those pieces let AI compare the failure against the intended behavior rather than merely interpret the error in isolation.
The Error Can Point at the Victim
An important limitation is that programs often fail downstream from the original mistake.
A function may throw an error because it received bad data created several steps earlier.
A missing file may be caused by a path built incorrectly elsewhere.
A database operation may fail because an earlier migration did not run.
So “the error says line 87” does not necessarily mean line 87 created the problem.
AI Is Useful for Explaining the Error
Even when it cannot immediately solve the issue, AI can translate technical error output into a useful hypothesis.
What does the exception mean?
What values should we inspect?
What surrounding code is relevant?
What is the smallest test that would distinguish between two likely causes?
That turns debugging into an evidence-gathering process.
Test the Hypothesis
A suggested fix remains a hypothesis until the code runs.
If the first fix fails, the failure itself becomes new evidence.
That iterative loop is one of AI's strongest technical uses:
error → hypothesis → patch → test → new evidence.
The model can move through the loop quickly as long as we keep giving it the actual results.
Worthy Trying Verdict
Worked: Frequently
Best evidence: Exact error + relevant code
Does the error always reveal the root cause? No
Human testing: Required
Best habit: Return the actual result of each attempted fix
The lesson: Don't summarize a technical error when you can provide the evidence itself.