Reading & Explaining Code with AI

Most of the Python code in your career will not be code you wrote. It will be code from a teammate, a Stack Overflow answer, a tutorial, an open-source library, or — increasingly — an AI assistant. Being able to read code is just as important as being able to write it.

AI is unusually good at this job. It can take an unfamiliar block of Python and explain it line by line, identify the design pattern, and even tell you whether the code looks suspicious. Used right, this turns AI into the patient tutor you wished you had in office hours.

What You'll Learn

• A prompt that turns AI into a code-walkthrough tutor
• The "what would change if..." technique for deeper understanding
• How to spot subtly wrong code in AI explanations
• Reading more advanced code: list comprehensions, lambdas, decorators

The Walk-Through Prompt

When you find unfamiliar Python, paste it into AI with this prompt:

I am a beginner. Please walk through this code line by line. For each line:

• say what it does in plain English
• explain any new concept I might not know
• point out anything that could break in real-world usage

[paste code]

This is your single most useful prompt for learning Python from real code.

Try it now with this snippet:

import pandas as pd

df = pd.read_csv("sales.csv")
df["date"] = pd.to_datetime(df["date"])
df = df[df["date"] >= "2026-01-01"]

monthly = (
    df.groupby(df["date"].dt.to_period("M"))["amount"]
    .sum()
    .reset_index()
    .rename(columns={"date": "month"})
)
print(monthly)

A good walkthrough explains: pd.to_datetime converts strings to date objects so date math works; the boolean filter keeps rows on or after Jan 1, 2026; groupby plus dt.to_period("M") groups by month; .sum() is the aggregation; .reset_index() turns the grouped index back into a regular column; .rename renames the date column to "month" for clarity.

If the AI omitted any of those steps, ask for a more thorough explanation.

The "What If I Changed X?" Technique

You learn faster by perturbing code than by reading it passively. Pick a line, then ask the AI:

What would happen if I changed dt.to_period("M") to dt.to_period("W")? Show me the difference in output.

The AI will explain that "W" means weekly periods instead of monthly, so you would see one row per week. You now understand to_period deeply, not just the recipe.

Try it with these prompts on the snippet above:

• "What if the amount column had missing values?"
• "What if date was already a datetime — could I skip the conversion line?"
• "What would df.groupby(df['date'].dt.year) return?"

Each answer teaches you something. After 20 of these "what if" questions across a few weeks, you stop needing them — your mental model of the library has filled in.

Spotting Wrong Explanations

AI is confident even when wrong. Three telltale signs that an explanation is off:

The explanation contradicts the code. AI says "this drops missing values" but the code does not call .dropna() anywhere. If the explanation references a step you cannot find, the AI is hallucinating.

The explanation invents a feature. AI says "this code uses pandas' built-in machine learning module," and you go look — pandas has no machine learning module. Verify any specific feature claim against the official docs (or ask Perplexity to find a source).

The explanation skips a tricky line. If line 7 is the most important line and AI said something vague like "this prepares the data," push back: "explain line 7 in detail."

The single best skill you can build with AI explanations is healthy skepticism. Ask "why?" twice. Ask "show me the proof" once. If the explanation cannot survive a follow-up question, the AI is bluffing.

Reading More Advanced Patterns

Some Python idioms are confusing the first time you see them. AI excels at decoding these.

List comprehensions

squares = [x ** 2 for x in range(10) if x % 2 == 0]

A list comprehension is a one-line for loop that builds a list. Read right to left: take each x in range(10), keep only the ones where x % 2 == 0 (even numbers), and put x ** 2 (the square) in the new list. Result: [0, 4, 16, 36, 64].

If this looks dense, ask AI:

Rewrite this list comprehension as a regular for loop, with comments. Then explain why the comprehension version is preferred.

You will see both versions side by side and learn the trade-off (comprehensions are concise; regular loops are easier to debug for beginners).

Lambda functions

sorted_students = sorted(students, key=lambda s: s["score"])

A lambda is an anonymous, throwaway function. Here it tells sorted to use each student's score as the sort key. Equivalent to:

def get_score(s):
    return s["score"]
sorted_students = sorted(students, key=get_score)

AI prompt to deepen understanding:

Show me three more places lambda functions are commonly used in pandas, with examples I can run.

You will get answers like df["price"].apply(lambda x: x * 1.05) for percentage adjustments and learn a tool you will use weekly.

Decorators

@functools.lru_cache(maxsize=128)
def slow_function(n):
    ...

The @something syntax is a decorator — a function that wraps another function to add behavior. The lru_cache example here memoizes results, so calling slow_function(5) twice only computes it once. Decorators are advanced; you do not need them for this course, but you will see them around. AI can demystify any decorator on demand.

A Practical Drill

Find a real piece of Python code online — a tutorial, a GitHub repo, a Kaggle notebook. Pick something 20 to 50 lines long that you do not fully understand. Paste it into Claude or ChatGPT with the walk-through prompt above. Then:

1. Read the AI's explanation.
2. Pick the line you understand least.
3. Ask "what would happen if I changed X?"
4. Pick the second-least-understood line.
5. Repeat.

Do this with one new snippet a day for two weeks. You will read Python far better than someone who only writes code from scratch.

Reading AI's Own Generated Code

When AI writes code for you, treat it like code you found online. Walk through it before running it. The discipline of reading every line of AI output is what separates a beginner who learns from one who copy-pastes their way to a nervous breakdown.

A short habit: after AI gives you 30 lines of code, paste it back into the chat with this question:

Walk me through your own code, line by line. Highlight anything that could fail with messy real-world data.

The AI will often catch its own bugs in this second pass. Use it.

Key Takeaways

• The walk-through prompt turns AI into a patient tutor for unfamiliar code
• Use "what if I changed X?" to perturb code and learn faster
• Healthy skepticism is your most important debugging skill — ask "why?" twice
• Ask AI to walk through its own code after generating it; this catches bugs
• Read at least one new code snippet per day for two weeks to build reading speed