The Codex Inefficiency Playbook

We use AI tools like OpenAI Codex to speed work, but they can add slow or insecure code. This playbook helps teams spot common OpenAI Codex inefficiency, fix it fast, and keep tech debt small. Use the 10-point checklist, code comparisons, and prompt templates the next time a PR includes AI-generated code.

Why this matters

LLM code generation can be a huge time-saver. But research and field reports show real problems: buggy patterns, repeated expensive operations, and security holes (see reports about Codex and related tools). A study of LLM-generated code found many inefficiency patterns that harm maintainability and performance. We want to ship safe, fast code—not just working code.

How to use this playbook

Run the checklist on every AI-generated PR.
Use the prompt templates when you re-run Codex for fixes.
Add a tiny repro to issues so fixes are fast.

Common Codex inefficiency patterns (and how to fix them)

Inefficient loops

Problem: Codex often writes clear but slow loops—extra allocations, repeated work, or O(n^2) patterns.

Fix: Combine work, use built-in methods, and avoid nested scans.

# Codex suggestion (Python)
result = []
for item in items:
    if expensive_check(item):
        result.append(transform(item))

# Optimized
result = [transform(x) for x in items if expensive_check(x)]

Repeated expensive operations

Problem: Codex may call the same DB query or API inside a loop instead of batching or caching.

Fix: Batch queries or cache results outside the loop.

// Codex suggestion (JS)
for (let id of ids) {
  const row = await db.query('SELECT * FROM users WHERE id = ?', [id])
  rows.push(row)
}

// Optimized: single batch
const rows = await db.query('SELECT * FROM users WHERE id IN (?)', [ids])

Memory-heavy data copies

Problem: Copying large objects or building huge lists without streaming.

Fix: Stream results, yield generators, or process in chunks to reduce peak memory.

Blocking I/O and sync calls

Problem: Codex sometimes mixes sync and async patterns causing blocking behavior.

Fix: Make I/O consistently async or offload to worker threads.

Deprecated or risky APIs

Problem: Codex can suggest deprecated functions or insecure defaults.

Fix: Replace with modern, secure APIs and add linting rules that catch known risky patterns.

10-point Code Review Checklist (use this on PRs)

Does the code have repeated expensive calls inside loops? If yes, batch or cache them.
Are there obvious O(n^2) patterns? Try to reduce complexity.
Is memory usage excessive? Replace copies with streaming/chunks.
Is I/O blocking the event loop? Convert to async/parallel where safe.
Any deprecated or unsafe APIs used? Replace and document reasons.
Are edge cases and error paths covered? Add guards and tests.
Is the implementation modular and testable? Break into functions where helpful.
Is sensitive data handled safely? Check for leaks and logging of secrets.
Do benchmarks or profiling show regressions? Run a targeted profile.
Did you run the optimization prompt on the same snippet and compare results?

Quick tip: Add this checklist to your PR template so reviews catch AI-generated code issues early. We do this at our org and it saves debugging time.

Prompt templates to get better Codex output

Below are short, targeted prompts you can paste into Codex when you want optimization, not just a rewrite.

Optimize a loop

"Optimize this Python loop for memory and speed. Explain why your version is faster. Keep behavior identical and keep tests passing."

Find repeated expensive operations

"Find repeated expensive operations in this function and suggest caching or batching opportunities. Point to exact lines and give code suggestions."

Suggest faster DB batching

"Suggest a faster way to batch DB queries in this function. Show code before and after. Use parameterized queries for safety."

Side-by-side example: slow vs optimized

Example: a handler that repeatedly queries a DB per user instead of batching.

// Slow (Codex produced)
async function enrichUsers(userIds) {
  const out = []
  for (const id of userIds) {
    const u = await db.getUser(id)
    out.push(await db.getUserDetails(u))
  }
  return out
}

// Optimized
async function enrichUsers(userIds) {
  const users = await db.getUsersBatch(userIds)
  const details = await db.getDetailsBatch(users.map(u => u.id))
  return merge(users, details)
}

Operational steps to add to your workflow

Add an "AI-generated" label for PRs so reviewers run the checklist.
Enable profiling on critical paths; re-run when AI changes code.
Add lint rules and CI checks for known inefficiency patterns.
Store common prompt templates in your repo (AGENTS.md or docs) so everyone uses the same constraints.

Security and compliance notes

Research shows tools like Copilot and Codex can produce vulnerable code patterns. Treat AI output like untrusted code: review for CWE-style issues, run security scans, and avoid blindly trusting suggestions for auth, crypto, or input validation.

Reference: see commentary on vulnerabilities and model limitations in OpenAI Codex and the inefficiency patterns cataloged in academic work such as arXiv: Unveiling Inefficiencies in LLM-Generated Code.

When to re-run Codex vs. rewrite yourself

Re-run Codex when the change is local (a loop, a query, a helper function). Rewrite when the code touches core invariants, security, or cross-cutting concerns. Use prompts that constrain design ("use streaming, keep API stable, add tests").

Final checklist and next step

We included a 10-point checklist and practical prompts. Next step: pick one recent AI-generated PR, run the checklist, and try a single optimization prompt. Share results with your team so prompts and rules get better over time.

We’ve got your back—keep PRs tight and performance sane. Quick experiment: take one small AI-generated function this week, run the "Optimize a loop" prompt, and compare perf before/after. Tell the team what changed.

Further reading: How OpenAI uses Codex, analysis pieces on Codex 2.0, and studies on generated-code inefficiencies.

The Codex Inefficiency Playbook