𝗖𝗹𝗲𝗮𝗻𝗶𝗻𝗴 𝗠𝗲𝘀𝘀𝘆 𝗗𝗮𝘁𝗮 𝗪𝗶𝘁𝗵 𝗟𝗟𝗠𝘀

𝗖𝗹𝗲𝗮𝗻𝗶𝗻𝗴 𝗠𝗲𝘀𝘀𝘆 𝗗𝗮𝘁𝗮 𝗪𝗶𝘁𝗵 𝗟𝗟𝗠𝘀

I handled a system for emails. Emails had invoices and orders. Formats varied.

I tried regex first. I wrote patterns for a few vendors. It worked for a while. Then vendors changed layouts. The regex broke. I spent weeks fixing patterns. One fix broke other cases. It was a mess.

I tried other ways. Templates failed. Custom models took too long.

I tried LLMs. I defined a schema. I sent raw text to the model. The model returned JSON. It worked immediately. No patterns. No training.

I found a few problems. Costs grew with volume. Calls took seconds. Models made mistakes.

I fixed this by:

• Batching calls to save money.
• Moving tasks to a background queue.
• Adding validation rules.

Now I use a hybrid system. Use regex for standard emails. Use LLMs for messy text. This saves money. It saves time.

My advice for you:

• Keep your schemas clear.
• Validate all output.
• Start with small models.

Source: https://dev.to/__c1b9e06dc90a7e0a676b/struggling-with-text-extraction-heres-how-i-finally-cleaned-up-messy-data-1nab Optional learning community: https://t.me/GyaanSetuAi