Teaching Generative AI to Classify Email Responses

Generative AI is a flexible tool that can be used in a variety of ways to help sales teams.

In many cases, we see AI used to write emails from scratch, summarize large amounts of information, and run simple calculations.

Previously, I’ve touched on how AiSDR uses generative AI to clean messy CSV files and even check another AI’s output.

But today, I’ll take this a different route and outline how we teach AI to classify email responses.

TLDR: 

• The goal: Sort incoming email responses automatically
• The tactic: Teach generative AI how to classify emails
• The result: Greater efficiency and time savings by no longer individually checking each email

Why AI outreach depends on proper reply classification

Not every reply is worth a follow-up. Some leads want a demo. Others just say they’re not interested in more emails. And some replies are nothing but out-of-office notices. Sorting all that by hand? Too much of a time waste. 

AI helps you spot what matters. It picks up real interest, kicks off follow-up steps, routes support tickets, and skips the out-of-office clutter. Your team spends less time digging through replies and more time moving conversations forward.

But before you can teach AI how to respond, you have to show it what it’s looking at. Most replies fall into a handful of clear categories. Once you know them, you’re halfway there.

Types of AI email classification: generative AI vs. rule-based

Once you’ve started collecting email replies, the next step is teaching AI how to sort them. Two common ways to do it: rule-based logic and generative models.

Rule-based systems follow fixed instructions.
If the reply says “unsubscribe,” tag it negative.
If it says “thanks, but no thanks,” – same thing.

They work well when replies are short and predictable, like “Not interested” or “I’m out of office.” But they fall short when there’s nuance. A message like “Circle back after our budgeting talks” won’t trigger any rule, even though it clearly means “not now, but later.”

Generative AI, like the models we use at AiSDR, works differently. Instead of scanning for keywords, it reads the whole thread, picks up on tone, phrasing, and intent, and classifies based on patterns it’s learned from real replies.

For example, AiSDR can detect replies like “I’ll review this next week, thanks,” automatically pause outreach, then resume when the lead becomes active again. No explicit rule needed.

When to use what:

• Use rule-based AI for straightforward patterns: out-of-office replies, clear opt-outs, yes/no answers.
  
• Use generative AI for more open-ended messages like “Let’s reconnect in Q4” or “Sounds interesting, but I’ve got questions.” 

Generative AI helps SDRs make sense of these in-between replies, so valuable leads aren’t skipped just because the intent wasn’t obvious at first glance.

You don’t have to pick just one approach. Rules handle the obvious. Generative AI fills in the gaps, so every reply leads to the right next step.

So you know why classifying replies matters and when to use each approach. Now it’s time to set up AI to handle it, and that starts with practical teaching steps.

Step 1: Collect a set of typical email responses

The key to teaching AI to do whatever you want is the dataset.

In this case, we need a large collection of email responses, complete with the original message and any follow-ups (more on why later).

Here are some types of responses you might want to collect:

• Yes
• No 
• Not interested
• Auto-replies (especially out-of-office messages)
• Let’s chat
• Send me a link

You can extend this list as much as you need, so long as you have the corresponding emails.If you don’t have any of your own emails, you can use email templates and simply create your own hypothetical responses.

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Step 2: Create clear “buckets” for classification

Once you have your email dataset in hand, it’s time to start classifying them.

I recommend starting from three categories or “buckets” for clearer differentiation:

• Positive
• Negative
• Maybe

Just enough to help the model separate signal from noise. But real inboxes aren’t always that tidy, so you’ll probably need to go a level deeper.

The most common types of email replies

Real inboxes rarely stick to just yes, no, or maybe. In practice, replies break into more specific types, and that’s what makes them useful for training. The more clearly you define these categories, the easier it is for AI to go from vague labels to specific actions.

Here’s what we see most often:

• Interested / Positive
  “Let’s book a call,” “Sounds good,” “Can you send the deck?”
  
• Not now / Follow up later
  “Circle back next quarter,” “Try me in a few weeks,” “Q4 works better”
  
• Not interested / Unsubscribe
  “No thanks,” “Take me off your list,” “We’re not looking”
  
• Wrong contact / Forwarded
  “This isn’t my area,” “Passing this to [Name],” “You should reach out to marketing”
  
• Out of office / Auto-reply
  “I’m no longer with [company name]. Please reach out to [colleague’s name, email/phone],” “I’m out until Monday,” “This inbox isn’t monitored”
  
• Questions / Objections
  “How do you compare to [Competitor]?”, “Is this GDPR-compliant?”, “What’s the pricing?”

Once you’ve mapped out the common reply types, the next step is to show the model exactly what fits where and what doesn’t.

Step 3: Outline which responses fit and which don’t

For each bucket, you’ll need to indicate which emails fit and which don’t.

Here’s a simple example of email responses that might fit the “Positive” and “Negative” buckets.

Then whenever you encounter a new email that fits the “Positive” or “Negative” bucket, you add it. 

For instance, if someone writes back “Sounds cool!”, you would add it to the “Positive” bucket. Likewise, you would add “Stop sending me emails” to the “Negative” bucket.

Step 4: Provide all context for classification

When teaching AI to classify email responses, context plays a role in two different ways:

• Instructions about what role the AI is fulfilling (i.e., “You are a sales representative responsible for reviewing email responses. You should categorize emails…”)
• Complete email conversation from the original message through all subsequent responses

With full context, generative AI should improve at classifying emails. This is also why I mentioned in Step 1 to collect the entire email conversation (original + all follow-ups + all responses).

Bonus tip: Set the stage for better results

Due to how large-language models work, AI’s output will yield more reliable results if you ask AI to provide its answer with the message it classified.

The reason why this works is that LLMs have to predict the next token. By forcing AI to essentially ‘repeat the question’, the data is fresher and more contextualized.

And like I mentioned earlier, AI thrives when it has sufficient context.

The Result

Here are the results of teaching AI how to classify email responses:

• Automate email outreach from first touch to demo (or closed)
• Scan email responses more efficiently by type of response
• Prioritize follow-ups based on positive and maybe replies
• Improve AI’s performance and accuracy over time

AI gets you there, but like with any smart system, the setup matters. I’ve seen where things can get tricky, and I’ve picked up a few reliable workarounds. 

Challenges in teaching AI to classify email responses

Even with a clean dataset and clear categories, email replies aren’t always easy to decode. Here’s what tends to trip the model up:

• Ambiguity and tone detection
  A reply like “Interesting.” Is that curiosity or a brush-off? Tone matters, and it’s not always evident from the text alone.
  
• Multiple intents in one message
  Think: “Not now, but maybe next quarter. Also, what’s your pricing model?” Is that a delay, a soft yes, or a request for more info? Often, it’s all three.
  
• Misspellings or informal language
  AI doesn’t always love “thx but nah”, “l8r maybe”, or “hit me up next szn.” The more casual the language, the easier it is to misread intent.
  
• Lack of labeled data
  Especially when working with niche industries or startups, you’ll rarely have a perfect dataset. Without examples, the model’s guesses get weaker.
  
• Domain-specific phrasing
  A phrase like “We already use an ATS” makes perfect sense to a recruiter, but unless your model has context, it won’t catch that it means “we’re not interested.”

How to improve AI email classification accuracy

If your model mislabels replies, don’t scrap it; you’ll have to adjust it. Here’s what actually works:

• Use a mix of labeled and unlabeled datasets
  Start with labeled replies like “Let’s talk” = positive, “Not now” = follow-up. But add unlabeled messages too, even ones the model hasn’t seen before. It helps catch informal phrasing like “circle back later?” or “touch base Q4.”
  
• Fine-tune with real customer responses
  Don’t rely on clean templates. Feed in messy, typo-filled replies like “i think we already use smth similar” or “send smth and i’ll show to team.” These teach the model how people actually write when replying fast.
  
• Use few-shot prompting techniques
  Instead of retraining, include a few labeled examples in your prompt. For instance:
  “Reply: ‘Not interested right now, maybe in Q1’ → Category: Follow up later”
  This sets the tone and helps the model generalize better.
  
• Fix high-impact mistakes first
  If the model keeps tagging “Not now, but send me info” as negative, that’s a missed opportunity. Flag these mislabels manually. Reclassify them. Feed them back in. Start with common edge cases.
  
• Build in user feedback
  Let your team correct replies inside your CRM or outreach tool. If someone changes a label from “Negative” to “Follow-up,” log it. Use those corrections as new training samples — the system learns faster when users teach it.

Each of these steps can be done without major tech changes, and they compound over time.

Real-world use cases for email response classification

Among AiSDR clients, we work with teams across SaaS, recruiting, logistics, and so many more. I’ve pulled together the common spots where they say classification makes a real difference and how they put it to use in day-to-day workflows:

B2B sales follow-ups

Sales teams often get replies like “circle back in a few weeks”, “send me more info,” or just a forwarded email to a colleague. 

Classification tags these as “Follow-up” or “Forwarded,” so they’re automatically slotted into the right next sequence, no rep needed to sort them manually.

Recruiting and HR workflows

In recruiting, responses often range from “still interested, just need a few days” to “already signed elsewhere.” 

Classification helps recruiters quickly spot which candidates to nurture and which pipelines to close. It also flags internal referrals or when someone forwards the role to a better-fit teammate.

Product feedback collection

Teams running outreach or onboarding campaigns often get unstructured product feedback buried in replies. 

Classification pulls out “we really need dark mode” or “found a bug in the dashboard” and routes them to product or QA instead of letting those messages sit unread in a sales inbox.