Trying to automate this SKU coding mess with a Python script has been a massive headache because the data handling part is just absolute garbage right now. I'm basically trying to scrape product descriptions from a CSV and turn them into these weird standardized strings like 100-010-8kg-Active, but the DX of trying to pipe this into an LLM without everything exploding is zero. I've got the OpenAI SDK doing the actual heavy lifting for the text mapping, but since the dataset is massive, my current logic of loading it into a Pandas DataFrame and then manually partitioning rows into chunks feels like a complete hack. I’m constantly fighting token limits and timeouts, and the thought of trying to "stitch" these processed chunks back into the original DataFrame without the index alignment going to hell is giving me actual anxiety. If I mess up the merge, I'm just going to end up with a corrupted CSV full of duplicate rows and I'll want to throw my monitor out the window. And don't even get me started on the latency—doing an API call for every single row is going to take literal ages unless I figure something out. I don't know if there's some secret Pandas trick I'm missing or if I just need to go full async with the OpenAI client to stop this thing from crawling at a snail's pace. I'm terrified that if one API call fails mid-batch, the whole process is going to wreck my data integrity. Here is the current prototype that is basically just a bottleneck in a trench coat:

import os 
from dotenv import load_dotenv
from openai import OpenAI 
load_dotenv() 
OPEN_API_KEY = os.getenv("OPEN_API_KEY")
client = OpenAI(api_key = OPEN_API_KEY)
with open("./utilities/instructions.txt") as inst: 
    instructions = inst.read() 
This is where the bottleneck happens

new_sku_code = client.responses.create(
    model = "gpt-4o", # Using current available models
    instructions = instructions,
    input = "Active - Champion Kettlebell 8kg" 
)
print(new_sku_code.output_text)

Has anyone actually dealt with large-scale data transformations using LLMs and Pandas without losing their mind? I need a way to make this partitioning and merging process actually robust so I'm not just praying to the dev gods every time I run the script.