Hey everyone, I’ve been trying to improve some data quality checks by using an LLM to flag anomalies in our customer support logs. I have 5 different system prompt variations I want to test to see which one gives the most accurate and useful flags.
My current setup is a simple Python script that loops through the prompts and runs them sequentially against a sample dataset, but it’s super slow. I’m using the OpenAI API, and waiting for each one to finish before starting the next feels like watching paint dry 😅. I need to compare results across all 5 prompts on the same 1000 text snippets.
I’m thinking there must be a better way. I use Airflow for orchestration at work, but this feels like overkill for a one-off test. I’ve heard about using `asyncio` or maybe the `concurrent.futures` module, but I’m a bit lost on the best pattern. My constraints are:
* I need to log which prompt produced which response.
* I have a tight rate limit on the API (requests per minute).
* I’d like to avoid spinning up complex infrastructure.
What’s the simplest, most straightforward way to run these 5 prompts in parallel? Should I:
* Use `threading` or `asyncio` directly in a script?
* Write a small DAG that fires off 5 tasks?
* Is there a lightweight library built for this kind of prompt testing?
Any advice or examples of how you’ve done this would be a lifesaver. I’m worried about messing up the API rate limiting or losing track of the prompt-response mapping.
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I'd lean toward threading with a ThreadPoolExecutor for this one, mainly because the OpenAI Python client is synchronous under the hood. Asyncio can work if you wrap the calls properly, but for a quick one-off test, threading is simpler to reason about and you don't have to deal with an event loop.
The trick is your rate limit. You can pass a shared semaphore to each thread so you don't exceed the API's requests per minute. Something like a simple time-based token bucket in the worker function, or just a pause after every N calls. That way you can fire off all 5 prompts against the same 1000 snippets in parallel, but still respect the cap.
One thing to watch out for: logging which prompt produced which response. Make sure your worker function returns a tuple of (prompt_id, response) and then collate them after the pool completes. I've seen people accidentally lose that mapping when they write to a shared list without a lock.
Also, if your rate limit is really tight, you might get more throughput by sending the same prompt to multiple snippets in parallel rather than running all 5 prompts at once. That way you can saturate the limit per prompt without mixing up the results. Which approach are you leaning toward?
Concurrent futures with ThreadPoolExecutor is indeed the pragmatic choice here, but I'd refine the approach on rate limiting. Wrapping synchronous HTTP calls in threads is straightforward, but a simple semaphore won't address the OpenAI tiered limits (requests per minute AND tokens per minute). You need client-side throttling that respects both.
Instead of baking logic into workers, use the `tenacity` library with a `wait_chain` for backoff on rate limit errors (status 429), and pair it with a global token bucket regulator for preventive throttling. This separates the retry logic from the concurrency mechanism. Also, for logging, don't just return a tuple - yield results incrementally to a thread-safe queue or write directly to a SQLite in-memory database with a connection per thread. This prevents memory bloat with 1000 snippets * 5 prompts.
One caveat: the official OpenAI client's internal session can cause contention under high concurrency. Consider creating a separate `requests.Session` or client instance per worker thread to avoid the GIL bottleneck on connection pooling.
Since you're already leaning towards `concurrent.futures`, that's the right call for a quick script. The key is pairing it with the official OpenAI library's built-in retry logic. You don't need `tenacity` or a custom semaphore if you instantiate the client with `max_retries` and have a paid account; it handles 429s automatically.
Structure your script around a `ThreadPoolExecutor` with, say, 5 workers. Pass a `(prompt_id, prompt_text, snippet)` tuple to each submission. Have the worker function return that same tuple plus the response. Use `as_completed()` and write results immediately to a CSV. This gives you live logging and avoids memory issues with 1000 snippets.
Your main bottleneck will be tokens/minute, not requests. Threads will fire concurrently, so you'll hit that limit fast. A simple, crude fix is to batch your 1000 snippets into chunks of 20 and add a `time.sleep(60)` after each batch. It's not elegant, but for a one-off, it's simpler than implementing a token bucket.
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