𝗪𝗵𝘆 𝗥𝗲𝗴𝗲𝘅 𝗙𝗮𝗶𝗹𝗲𝗱 𝗮𝗻𝗱 𝗟𝗟𝗠 𝗙𝘂𝗻𝗰𝘁𝗶𝗼𝗻 𝗖𝗮𝗹𝗹𝗶𝗻𝗴 𝗪𝗼𝗿𝗸𝗲𝗱

𝗪𝗵𝘆 𝗥𝗲𝗴𝗲𝘅 𝗙𝗮𝗶𝗹𝗲𝗱 𝗮𝗻𝗱 𝗟𝗟𝗠 𝗙𝘂𝗻𝗰𝘁𝗶𝗼𝗻 𝗖𝗮𝗹𝗹𝗶𝗻𝗴 𝗪𝗼𝗿𝗸𝗲𝗱

I spent three days writing regex to parse doctor emails. I wrote 400 lines of code. It worked for two formats. A third clinic joined. The code broke.

I needed structured data. I wanted dates, times, and names. The sources were messy. I had emails and Slack messages.

Old methods failed:

• Regex is brittle.
• Keywords miss context.
• NLP models need too much labeled data.

I switched to OpenAI function calling. I define a JSON schema. The model fills the schema. It returns a structured object.

I tested 50 emails. It handled these:

• Different date formats.
• Varied time formats.
• Missing fields.
• Different address styles.

Accuracy was 92%. The errors were relative dates like next Monday. I added the current date to the prompt. This fixed the issue.

There are trade-offs:

• Each call costs about 0.1 cents.
• Response time is 1 to 2 seconds.
• It is not for real-time typing.

But it saves hours of debugging.

Tips for your setup:

• Use a strict schema.
• Add examples to your system prompt.
• Validate the output.
• Provide today's date for context.

This works for invoices or meeting notes too. Use it for any messy text. Stop fighting with regex.

Source: https://dev.to/__c1b9e06dc90a7e0a676b/why-my-regex-based-parser-failed-and-how-llm-function-calling-saved-me-e01 Optional learning community: https://t.me/GyaanSetuAi