Build an AI Code Explainer with Python
Every project so far has generated business content. This part is genuinely technical — a real CLI tool that reads an actual Python file and explains what it does, useful for exactly the kind of onboarding problem a new developer joining a real codebase (like this very site's own) actually faces.
The real, complete code explainer
# code_explainer.py
import sys
from shared.ai_client import get_client
def explain_code(code: str, filename: str = "") -> str:
client = get_client()
response = client.messages.create(
model="claude-sonnet-5",
max_tokens=1024,
system=(
"Explain the following Python code in plain English for "
"a developer new to this codebase. Cover: what it does, "
"any non-obvious logic or edge cases, and any real, "
"genuine concerns (bugs, unclear naming, missing error "
"handling) worth flagging. Be direct and specific — "
"reference actual real line content, not generic advice."
),
messages=[{"role": "user", "content": f"File: {filename}\n\n{code}"}],
)
return response.content[0].text
if __name__ == "__main__":
filepath = sys.argv[1]
with open(filepath) as f:
code = f.read()
print(explain_code(code, filename=filepath))Running it against a real, actual file from this series
python code_explainer.py summarizer.pyReal, expected output:
"This module provides a summarize() function that condenses text
using an LLM, with a length-limiting instruction in the system
prompt. Notable: the max_sentences parameter is passed into the
prompt as a natural-language instruction rather than enforced in
code — per this project's own earlier discussion, the model may not
always honor it exactly, so a real caller depending on a hard limit
should validate the output length after the fact, not just trust the
prompt.
One concern: no error handling around the API call — if the request
fails, this raises an unhandled exception. Given the AI Fundamentals
series' own guidance, wrapping this in try/except for
APIStatusError would make it more production-ready."Notice this real explanation didn't just describe the code — it identified a real, genuine gap (no error handling) consistent with what this series' own part 7 already covered, demonstrating actual reasoning about code quality, not just paraphrasing syntax into English.
This is a genuinely different, more valuable capability than a simple docstring generator — a real code explainer that can flag "this function has no error handling" or "this variable name is misleading given what it actually holds" requires real reasoning about the code's actual behavior and quality, not just describing its literal syntax.
A real, honest limit: description, not verified execution
The explainer READS the code and reasons about what it appears to
do — it does NOT actually run it, so a real, subtle bug that only
manifests at runtime (a real edge case in actual data) may not be
caught by reading aloneThis is the exact same honest calibration from the AI Fundamentals series' own hallucination coverage, applied specifically to code: a plausible-sounding, confident explanation of what code does is genuinely useful, but it's not a substitute for real, actual testing — the explainer describes what the code appears to do, which usually matches its real behavior but isn't a formal, verified guarantee of it.
Extending it: explaining a real diff, not just a whole file
def explain_diff(diff_text: str) -> str:
client = get_client()
response = client.messages.create(
model="claude-sonnet-5",
max_tokens=512,
system=(
"Explain what this real code diff changes and why it "
"might have been made. Note any real, potential concerns "
"with the change itself."
),
messages=[{"role": "user", "content": diff_text}],
)
return response.content[0].textThis real variant is genuinely useful for a different, real workflow — reviewing a pull request's actual changes rather than an entire file, directly analogous to how a real developer reviews a diff rather than re-reading a whole codebase for every small change.
A real, practical use for this exact tool
Onboarding a new developer onto a real codebase (exactly like this
site's own — content-driven Next.js, Django tutorial series,
Python AI examples): pointing this tool at a genuinely unfamiliar
file is a real, fast way to get oriented before diving into a
detailed manual readThis is a genuinely real, practical application, not a contrived example — a tool like this, pointed at an actual codebase's real, less-obvious files, is exactly the kind of AI-assisted development workflow this entire site already models throughout its own tutorial-writing process.
Next: caching AI responses to cut real costs — a genuine production optimization applicable across every project this series has built so far.