Illustrative learning material. Examples are not presented as actual model outputs or completed tool tests.

An explanation is a hypothesis

An AI-generated explanation becomes useful when you can connect its claims to the program. Split the response into small statements, identify the evidence for each, and check the cases that could disprove it.

This guide uses a manually reasoned source-code example. It does not present an actual model response or an independently measured tool result.

Work through a concrete example

unsigned int checksum(const char *s) {
    unsigned int n = 0;
    while (*s)
        n += (unsigned char)*s++;
    return n;
}

An appropriate narrow description is “the function adds the unsigned byte values before the first null byte.” Each part of that sentence has evidence:

  • n = 0 establishes the initial accumulator.
  • while (*s) stops the loop when the current character is zero.
  • (unsigned char) makes the interpretation of each added byte explicit.
  • s++ advances the pointer after reading that character.
  • return n returns the accumulator.

The code alone does not justify calling this encryption, a secure digest, or a reconstruction of original source.

Use normal and boundary cases

On a small screen, scroll the table to compare →
InputExpected resultWhat it checks
Empty string0Initial state and zero iterations
ASCII a97A single iteration
ASCII abc294Accumulation and pointer progress
ASCII acb294Order is not preserved by this sum

The last row is particularly useful: it exposes a property a vague “checksum” label might hide. Long-input behavior also depends on the width of unsigned int; record the actual compilation environment before making claims about overflow.

Look for overreach

When an explanation introduces names such as “authentication,” “license check” or “malware,” ask which evidence establishes that role. A local arithmetic pattern may be real while the larger story is still a guess.

For compiled programs, check that the cited location is actually on the path you are investigating. A nearby function, a suggestive string, or a model-generated variable name is a clue rather than a complete argument.

Preserve the uncertainty

Use three explicit notes: observed behavior, interpretation, and unresolved questions. If you cannot run the program, say which conclusions are static observations. If you can run it, preserve the input and the environment that produced the observation.

To start a new investigation, follow the introductory workflow. If a model is connected through a plugin or agent, use the tool profiles to keep host dependencies and model data flow separate.

Sources & further reading

Tools in this guide