Performance Bugs in AI-Generated Code

# AI gives you this — looks fine, ships green:
def get_user_emails():
    users = User.objects.all()  # 1 query
    return [
        {"id": u.id, "email": u.email, "team": u.team.name}
        # u.team.name triggers a query per user. 10k users = 10,001 queries.
        for u in users
    ]

# The fix: prefetch / select_related
def get_user_emails():
    users = User.objects.select_related("team").all()  # 1 join query
    return [
        {"id": u.id, "email": u.email, "team": u.team.name}
        for u in users
    ]

# Same code, 10000x faster on large data.

# Prepend to any prompt where data size matters:

"This function will run on 100k+ rows in production.
Constraints:
  - Must complete in under 200ms.
  - O(N log N) or better.
  - No N+1 queries — use joins/IN clauses.
  - Stream the result if it doesn't fit in memory.
  - Add a comment with the expected complexity."

# 1. Run a profiler on the slow function (cProfile, py-spy, clinic.js, etc.)
# 2. Paste the profiler output into chat:

"Here is py-spy output for a function that takes 8s on 100k rows.
The top 3 hot spots are <paste>. Suggest the smallest possible change
to each that would speed it up. Show before/after for each."

# AI is excellent at reading flame graphs and profiler output.
# This is one of its highest-value uses for performance.

# After AI writes the function, immediately:

"Write a microbenchmark that runs this function on:
  - 100 items (warm-up)
  - 10k items
  - 1M items
  Report time per call and memory peak. Use timeit + tracemalloc."

# 60 seconds of work, surfaces 80% of perf bugs before they ship.