A program can produce the right answer and still contain work that does not help it reach that answer. Tests pass, the output looks correct, and unnecessary computations survive because they appear harmless.
This becomes easier to miss in AI-generated code. A model can produce a plausible implementation in seconds, but plausible code often includes variables, conversions, or branches that the requirement never asked for.
An intent alignment review adds one question to the usual correctness check:
Does every instruction help achieve or explain the stated goal?
This does not require a formal proof or an exhaustive line-by-line exercise. The useful result can be concise.
Correctness and intent
Correctness asks whether the observable behavior matches the specification. Intent alignment looks for code that contributes neither behavior nor useful clarity.
The goal is not to produce the fewest possible lines. A named constant or helper function can be worthwhile even when the program could run without it. The concern is accidental complexity: code that suggests requirements or design decisions that do not actually exist.
AI can help by reading the requirement and implementation together. It can confirm the working behavior, identify unnecessary instructions, and explain whether those instructions are harmful or simply unhelpful.
A small Fibonacci example
Consider this specification:
The function should print to stdout the first hundred elements of the Fibonacci sequence.
The phrase "first hundred" does not specify whether the sequence begins with 0, 1 or 1, 1. For this review, we assume the intended convention begins with 0, 1 and prints one value per line.
def print_fibonacci_100():
a, b = 0, 1
sequence_limit = 100
display_width = len(str(sequence_limit))
for index in range(sequence_limit):
current_value = int(a)
print(current_value)
a, b = b, a + b
checkpoint = (index + 1) % 10 == 0
final_pair = (a, b)
print_fibonacci_100()
Review
The implementation can be reviewed in one concise finding:
The Fibonacci generation itself correctly prints 100 values beginning with
0, 1.display_width,checkpoint,final_pair, andint(a)are unnecessary but do not affect correctness.
The finding separates the correct behavior from implementation noise.
display_width, checkpoint, and final_pair compute values that are never used. int(a) participates in the active output path, but the conversion is redundant because a remains an integer throughout the loop. Once checkpoint is removed, the loop index is unnecessary too.
A cleaner implementation for reference
def print_fibonacci_100():
a, b = 0, 1
for _ in range(100):
print(a)
a, b = b, a + b
print_fibonacci_100()
The original and simplified versions produce identical stdout: 100 values beginning 0, 1, 1, 2, 3, 5, following the Fibonacci recurrence, and ending with 218922995834555169026.
The shorter version is useful because its remaining instructions all have an obvious purpose: initialize the sequence, repeat 100 times, print the current value, and advance the pair.
A practical review standard
An intent alignment review can stay simple:
- Confirm whether the implementation satisfies the requirement.
- Point out instructions that add no required behavior or useful clarity.
- Distinguish harmless redundancy from correctness problems.
As AI-generated code becomes a larger share of software, this kind of analysis becomes more necessary. Correct behavior can still hide accidental complexity. The result is a review, not a formal guarantee: hidden requirements and wider system behavior may justify code that looks unnecessary in isolation.
Correctness tells us that the code works. Intent alignment asks whether the implementation contains only ideas we can explain and defend. In many cases, a precise paragraph and a cleaner reference are all that is needed.
