Why good comments in code matter today

We now use tools and models that read large code bases. These tools try to help with writing new code, completing code, or finding bugs. Such tools can only see code structure if there are no comments. If code has clear comments, they see more. They see intent, rules, and assumptions. This helps tools give better suggestions or bug fixes.

Good comments also help teams. When new developers join, comments help them understand the code faster. When someone inspects a bug or tries to extend a module, comments clarify what the code does and why. This reduces confusion and errors.

What makes a comment useful

A useful comment explains more than what the code immediately does. It tells what the inputs and outputs should be. It notes possible edge cases or limitations. It lays out assumptions and constraints. It explains why some logic matters.

It also stays up to date when code changes. A comment that stays from old code can mislead. A comment should keep pace with code.

Comments can also describe high-level steps or purpose of a function. They can show where code expects valid input. They can document side effects or error conditions.

Example: data processing with context

Suppose you have a function that normalizes a list of numbers. Without comments the code might look like this:

def normalize(values):
    total = sum(values)
    return [v / total for v in values]
        

This works for many cases. But if values is empty, or all values are zero, the code will give wrong results or error. A tool will not know what you expect. A comment helps:

def normalize(values):
    """
    values: list of non-negative floats
    return: list of floats
    if values is empty return empty list
    if sum of values is zero return original list
    this avoids division by zero
    """
    if not values:
        return []
    total = sum(values)
    if total == 0:
        return values
    return [v / total for v in values]
        

With comments, a tool or a human sees how to handle edge cases. A tool can reuse this logic or warn if someone uses the function wrongly.

Example: complex logic with manual steps

Imagine a function that parses user input, cleans data, transforms it, and stores results. Without comments, structure might be confusing. With comments, you show the steps and the purpose.

def handle_request(data):
    """
    data: dict with keys 'user_id', 'payload'
    Step 1: validate user_id exists and is integer
    Step 2: check payload is non-empty dict
    Step 3: transform payload keys from legacy format to new format
    Step 4: save result to database
    Return True if save succeeded, False otherwise
    """
    # validation
    if 'user_id' not in data or not isinstance(data['user_id'], int):
        return False
    if 'payload' not in data or not isinstance(data['payload'], dict) or not data['payload']:
        return False

    # transform payload
    new_payload = {}
    for key, value in data['payload'].items():
        new_key = legacy_to_new(key)  # assume this converts format
        new_payload[new_key] = value

    # save result
    return save_to_db({'user_id': data['user_id'], 'data': new_payload})
        

This way you tell clearly what you expect and what happens. Tools or code reviewers can follow logic easily.

Why this helps LLMs or automated tools

When code is clear and commented, models that read code get more information. They get not only what happens but also why. They get expectations about inputs and outputs. They get hints for edge cases. This helps them produce correct completions, avoid mistakes, and even generate tests or documentation.

Comments act like a simple contract or spec for code. They guide both humans and machines.

For example, if you later ask a tool to change logic or add validation, it knows what conditions must hold. If you ask a tool to trace runtime errors, comments help link code to expected behavior.

When comments can hurt

Comments that repeat what code does without adding meaning are not helpful. For example writing # add 1 over x += 1 is redundant. If code changes and comment stays, it becomes wrong. That misleads humans and tools.

Also, too many meaningless comments make code harder to read. Code reviews or automated tools become noisy.

Comments that contradict code can lead to bugs or wrong assumptions. That is worse than no comments.

How to build a commenting habit

When you write new code, add a short description of what the function does, what inputs it expects, what output it returns, and what special cases it handles.

When you update code, update comments too. Do not leave old comments behind.

During code review, treat comments like part of code. Ask if they match code logic. Ask if they add useful information. Remove comments that are no longer correct.

Treat comments as part of documentation. They help future developers and automated tools.

Conclusion

Clear comments help humans and machines understand code better. They show intent, rules, and assumptions. They make code easier to maintain, modify, and debug. They help tools give better suggestions or fixes.

If you want to use tools that read code, or just have a team that works on the same code base, good comments are worth the effort. Keep them relevant, simple, and up to date.