I'm working on a method to determine the status of application emails, which can be a bit tricky. Most technical positions send out automated emails with predetermined phrases. I'm looking for a more effective way to analyze these emails to categorize their statuses as either Rejected, Offer, or Interview.
Currently, I'm using Approach #1, where I've hardcoded certain keywords linked to each status and maintain a scoring system. I loop through these keywords, track their occurrences, and determine which status has the highest score. While this method works well with around 97% accuracy based on my own email samples, I worry about potential biases in my keyword choices and whether this method is too memory-intensive.
For Approach #2, I considered using an AI API like GPT or Claude to analyze the email body and decide the status. However, I'm hesitant as it seems like a lazy option—plus, I question the cost-effectiveness of making multiple API calls based on the number of emails I have. I'd love to hear any other strategies you might suggest, especially since I'm keen on keeping it more traditional than just throwing my question at a chatbot. Haha!
3 Answers
Totally get your concerns about relying solely on AI. Approach #2 isn't a bad option if you're looking for speed, but it's not the only way! Your first method definitely gives you the control you want, just be aware that fine-tuning may be necessary as the number of emails increases. And just so you know, using AI doesn't mean learning stops; it's about how you integrate it into your process.
Saying Approach #2 is definitely the way to go just isn’t accurate. It really depends on your needs! If you’re okay with a quick solution complemented by some human oversight, then it might suit your situation. But if you want to keep learning and challenge yourself, sticking with your first approach or even blending both methods could be the best route to take.
Using AI sounds like a potential shortcut, but if you prefer to have more control and learn as you go, your first approach is solid. Just be mindful that as your data grows, it might consume a lot of memory. A thought: consider using AI for an initial classification to help structure your data before applying your keyword method for more detailed analysis. This way, you can save some grunt work without losing the learning aspect. Also, I've used tools like Runable to visualize output data better, which might help you spot trends and patterns more easily!
Related Questions
How To: Running Codex CLI on Windows with Azure OpenAI
Set Wordpress Featured Image Using Javascript
How To Fix PHP Random Being The Same
Why no WebP Support with Wordpress
Replace Wordpress Cron With Linux Cron
Customize Yoast Canonical URL Programmatically