Prompt Template for Classifying Catch-All Emails with an LLM

Why Catch-All Emails Ruin List Hygiene

You send a targeted outreach campaign, only to see your hard bounce rate spike. You check your list, and one address keeps standing out: [email protected]. It’s not invalid — it accepts mail. But it’s not your recipient, either. That’s a catch-all email, and it’s quietly breaking your deliverability.

Catch-all domains accept every incoming message, no matter the address. They’re not a sign of good email hygiene — they’re a signal that the system isn’t tracking or managing recipients. That means spam traps, inflated bounce rates, and damaged sender reputation — all from one address in a high-volume list.

The real fix isn’t just checking for validity. It’s learning to classify catch-all emails accurately. That’s where a prompt template for classifying catch-all emails with an LLM comes in. You don’t need guesswork. You need structured, reliable detection.

Key takeaways

• Catch-all domains accept all emails, making them unsuitable for targeted outreach.
• They disproportionately inflate hard bounce rates and damage sender reputation over time.
• A prompt template for classifying catch-all emails with an LLM enables automated, accurate detection at scale.

What Does 'Catch-All' Really Mean in Email Verification?

A catch-all email address means the receiving server accepts messages for any email address—valid or not—at that domain. This behavior defeats standard validation, since the server never rejects a non-existent recipient, making it impossible to confirm if a specific user exists based on SMTP response alone. You might see a "success" from the server, but that doesn’t mean the intended person is real or reachable. This is a fundamental flaw in assuming all email validation works the same across domains.

Why Standard Checks Fail on Catch-All Domains

Most email verification tools rely on SMTP to test whether a specific address exists. But on a catch-all domain, the server says "yes" to every address, even fictional ones like [email protected]. This creates a false positive, leading you to believe every address is valid. The system works as designed—emails arrive—but not at the right person. This is why relying solely on SMTP validation isn’t enough for accuracy.

Modern verification services, including Emaillistchecker.io’s bulk verification, use deeper analysis to detect catch-all behavior by combining SMTP checks, domain reputation, and pattern recognition. We don’t just check if a server accepts mail—we examine whether the address pattern aligns with real user trends or is likely a placeholder.

How LLM-Powered Templates Help Classify Catch-All Behavior

Let’s be clear: you can’t rely on manual logic to flag every catch-all domain. But a well-designed prompt template for classifying catch-all emails with an LLM can analyze patterns across multiple addresses and identify inconsistencies. You feed the LLM a series of known addresses from a domain—some real, some obviously fake—and ask it to rate the domain's consistency.

For example, the prompt might say: “Based on the email patterns and response behaviors from this domain, does it likely use a catch-all policy? Explain.” The model uses known heuristics—like overly generic naming (e.g., [email protected]) or lack of name-based variation—to assess risk, even without direct access to the server’s internal config.

Because catch-all domains are common in enterprise and government setups, ignoring them leads to high bounce rates and poor sender reputation. Tools like Emaillistchecker.io’s API integrate this logic directly into real-time validation workflows, helping you avoid wasted sends. It's not about rejecting domains—it’s about knowing when you can’t validate an address reliably.

For more on how email validation accounts for real-world behavior, see RFC 5321, which defines SMTP’s basic behavior, including message acceptance rules. While it doesn’t prohibit catch-all setups, it does clarify what "acceptance" means—and when it doesn’t imply usability.

How LLMs Can Improve Catch-All Classification

Large language models can go beyond basic SMTP checks by analyzing patterns in server responses, domain behavior, and email infrastructure to detect catch-all setups with higher accuracy. They spot anomalies—like overly generic error codes or missing recipient validation—that signal a domain accepts all emails, even invalid ones. When trained on verified data, they assign probabilities to catch-all detection, reducing false positives common in regex or simple protocol checks.

Learning from Infrastructure Signals

Let’s be honest: standard checks often miss the subtle clues of a catch-all. A domain might respond with a “250 OK” to any email, even a random one—this isn’t just a technical detail, it’s a design choice. LLMs are trained to notice these patterns across thousands of domains, identifying anomalies in response timing, error message structure, and even DNS configurations. They don’t just follow rules; they learn what normal behavior looks like and flag deviations.

This works because SMTP isn’t just about sending mail—it’s about signaling intent. A server that says “User doesn’t exist” when a recipient is invalid is behaving as expected. But when it replies with “250 OK” regardless, that’s a red flag. LLMs detect that inconsistency by comparing response text, status codes, and timing across known good and bad patterns.

From Rules to Probabilistic Insights

Traditional tools rely on hardcoded lists or simple regex to flag catch-alls. But domains vary, and some misconfigured systems behave like catch-alls without being one. LLMs solve this by assigning probabilities—like a 92% likelihood a domain is catch-all based on its response pattern, not just one binary match.

That’s not magic. It’s training on datasets where each domain has been tested under real conditions. The more diverse and verified the training set, the better the model learns what constitutes a true catch-all versus a misconfigured server. This is why data quality matters—garbage in, garbage out, even for AI.

For teams running campaigns with hundreds or thousands of addresses, this kind of insight matters. You can’t afford to send to a fake, catch-all inbox that appears valid. You can reduce waste by using a service that leverages both real-time SMTP checks and deeper behavioral analysis. EmailListChecker’s bulk verification tool analyzes domains at scale and flags potential catch-alls—combining infrastructure checks with AI-driven analysis to improve your deliverability.

For developers, the same logic applies. An API-driven approach integrates seamlessly into your workflow, applying these insights in real time. The more you verify early, the fewer bounces and complaints you’ll see later. A catch-all isn’t just a delivery risk—it’s a reputation risk.

The Right Prompt Template for LLM Classification of Catch-All Emails

You can reliably classify catch-all domains using a structured prompt that combines SMTP behavior, DNS data, and past verification outcomes. Feed the LLM the domain, SMTP response codes (like 550 for invalid users), MX and SPF records, and past validation results. Ask for a JSON output with a binary catch-all verdict, confidence score (1–10), and a clear reason based on observed patterns. This approach reduces false positives and aligns with industry-standard techniques used by email verification services like those at Emaillistchecker.io.

Step-by-Step Process for Effective LLM Classification

1. Define the context clearly: Begin the prompt by instructing the model to analyze the domain’s email server behavior. Use real data—SMTP response codes, MX record alignment, SPF validity—to ground the evaluation in technical reality. Catch-all behavior is not guessed; it’s inferred from repeated 250 OK responses for invalid addresses, which is a known indicator in RFC 5321.
2. Specify the task with precision: Direct the model to classify whether the domain likely uses a catch-all system. Avoid vague phrasing like “assess potential.” Instead, say: “Determine whether the domain is almost certainly catch-all, and assign a confidence level from 1 to 10.” This ensures the output is machine-readable and measurable.
3. Structure input data to reduce ambiguity: Include the domain name, a list of SMTP response codes from validation attempts (e.g., 550 User not found, 553 Bad email), MX records, SPF records, and prior verification outcomes. The more input layers you provide, the more accurate the model’s inference.
4. Enforce a consistent JSON output format: Demand exactly this structure: {"is_catch_all": true/false, "confidence": 1-10, "reason": "brief explanation"}. This enables automated processing, reduces parsing errors, and ensures uniformity across bulk tests. Tools like Emaillistchecker.io’s API rely on such predictable formatting.

Include a real-world example: Show a concrete case:

{"is_catch_all": true, "confidence": 9, "reason": "SMTP 250 response for invalid users; no recipient validation observed across 3 attempts"}

This builds intuition and ensures the model learns from correct patterns, not hallucinations.

Why This Template Works for Real-World Verification

By combining technical signals—like consistent 250 responses to unknown users, which violate basic SMTP best practices—this method leverages industry-recognized heuristics. The confidence score acts as a sanity check: a score below 4 should trigger a manual review. This process avoids over-trusting the model and aligns with how leading verification services evaluate deliverability risks. It also integrates cleanly with tools like inbox placement testing, where catch-all domains often signal low sender reputation.

How to Validate the Effectiveness of Your LLM Prompt

You can validate your LLM prompt for classifying catch-all emails by testing it against a curated dataset of domains with known catch-all status, comparing its output to actual SMTP and DNS verification results, and tracking accuracy over time using a small, labeled test set. This ensures the model isn’t just guessing—it’s learning real signals.

Test Against Verified, Real-World Data

• Start with a dataset of 50–100 domains where you know the catch-all status—verified via tools like MXToolbox or DNSimple’s DNS lookup, which provide reliable DNS and MX record data.
• Run the LLM prompt on each domain and record its classification (catch-all, non-catch-all, uncertain) and confidence score.
• Compare the LLM’s output to known ground truth: a domain is catch-all if SMTP handshake attempts return success for arbitrary addresses (e.g., [email protected]).

Measure Accuracy Over Time & Iterate

• Track classification accuracy using a small, fixed test set with known outcomes—adjust your prompt if confidence is low on clearly catch-all or non-catch-all domains.
• Use the output to detect patterns in errors—e.g., if the model marks domains with widely accepted catch-all setups (like Gmail) as non-catch-all, refine the prompt to include stronger context about common patterns.
• Refine the prompt phrasing iteratively: add explicit examples, adjust temperature settings, or rephrase to emphasize specific signals like SMTP acceptance behavior or MX record structure.
• Re-validate after each change. Consistent confidence on clear cases (e.g., domains known to accept mail for non-existent addresses) is a sign of robustness.

Keep your test set small but stable—this lets you measure progress without noise. Over time, this process reveals whether your prompt generalizes well or misclassifies based on weak or misleading signals.

For bulk validation, use email list verification tools to test actual deliverability behavior at scale, which can help ground truth model predictions. You can also use the API to integrate real-time checks into your workflow, ensuring you’re not relying solely on LLM estimates.

Why Manual LLM Classification Isn't Enough for Bulk Verification

You can’t reliably classify catch-all emails at scale by running prompts manually. Doing so on thousands of addresses is slow, inconsistent, and prone to errors — especially when dealing with edge cases like greylisting, role accounts, or disposable domains. Automation isn't just faster; it’s more accurate and consistent. Tools like Emaillistchecker.io handle 100,000+ email verifications daily with 98.9% accuracy, using real-time infrastructure, not prompts.

Manual Prompts Break at Scale

Let’s say you want to classify catch-all domains using an LLM. You write a prompt. You run it on one email. That’s fine. But if you do it for 10,000 addresses, you're not just running a script — you’re asking an AI to process 100+ prompts per minute, with zero consistency between runs. Language models are noisy, and small changes in phrasing or context lead to different verdicts. There’s no guarantee a catch-all will be classified the same way twice.

And that’s before you factor in the cost of timeouts, rate limits, and API billing. Even with free-tier access, you’ll hit caps quickly. More importantly, you’re relying on a system that doesn’t know the actual behavior of SMTP servers or the state of DNS records. An LLM doesn’t send an actual email to test delivery — it guesses based on patterns. That’s not reliable for deliverability decisions.

Real Tools Use Real Infrastructure

Tools like Emaillistchecker.io work differently. They don’t rely on guesswork or prompts. They run real SMTP transactions, query DNS records (like MX and SPF), and analyze server behavior — including how servers respond to invalid addresses, which helps distinguish catch-alls from true bounces. They also track sender reputation, detect disposable domains, and account for transient issues like greylisting.

This layered approach means they can flag a catch-all not just by pattern, but by behavior: when a server accepts mail for non-existent users, it’s likely a catch-all. This is how industry-standard tools like those used by Return Path or Mail-Tester assess delivery risk. It’s not AI hallucination; it’s signal extraction from real protocols.

Want accuracy at scale? Use a system built for it. Instead of tuning prompts, automate with a service like Emaillistchecker.io. Its bulk verification and API workflows are designed for enterprise-grade processing. You get consistent, measurable results — no tuning, no guesswork. And since credits never expire, you’re not locked into a monthly cycle. Learn how it works: bulk verification or real-time API.

Catch-All Detection in Emaillistchecker.io: How It Works

Our system identifies catch-all domains by validating email addresses at scale: it checks MX records, performs real SMTP handshakes, and analyzes recipient-level responses. If multiple invalid addresses return a 250 (success) or 550 (user unknown) consistently, or if no validation occurs at the recipient level, the domain is flagged as catch-all. The verdict is returned with a confidence score and integrated directly into the core list hygiene engine—no add-on required.

How We Detect Catch-All Domains

Let’s take a step back—most tools only check if an email domain exists. We go further: we simulate actual delivery attempts using real SMTP protocols. For each address, we connect to the mail server, initiate a MAIL FROM command, and then test a RCPT TO with a known invalid email. This isn't theoretical; it's a standard way to probe server behavior, as described in RFC 5321, the foundational SMTP specification.

When the server responds with a 250 (recipient accepted) for a non-existent address, that’s a red flag. So is consistently returning 550 (user unknown) for every test, especially if the domain lacks per-recipient validation. These patterns suggest the server treats all incoming mail as valid—either because it's a catch-all or configured to mask errors. We detect this behavior across multiple test addresses to avoid false positives.

Integration Into Core Verification

This isn’t a feature bolted on. Catch-all detection is baked into the engine that checks validity, syntax, and deliverability. It runs in parallel with all other checks, so you don’t need to run an extra scan or adjust settings manually. If a domain is catch-all, we return that verdict with a confidence score based on response consistency and server behavior—no guessing.

For example, a domain like [email protected] might be rejected with a 550 if the recipient doesn’t exist, but a catch-all domain might accept it with a 250, even when the user doesn’t exist. We flag this inconsistency and report it clearly. This helps prevent misattribution, stops emails from being sent to fake or unclaimed addresses, and improves sender reputation over time.

Because this detection happens automatically and reliably, it’s one reason why over 98.9% of email lists processed through our system achieve higher inbox placement. You’re not just cleaning your list—you’re avoiding spam traps and blacklists, which often target domains that accept every email indiscriminately.

If you’re running campaigns on Mailchimp, HubSpot, Klaviyo, or SendGrid, this level of hygiene is essential. Our bulk verification and real-time verification API include catch-all detection as standard. Want to test deliverability before sending? Try our inbox placement to see how your emails land.

How to Use Emaillistchecker.io to Filter Catch-All Addresses

Upload your list or use the real-time API to verify emails, then review the results for "catch-all" or "risky" statuses. Exclude these domains from campaigns to reduce bounces and protect sender reputation. Use the in-app AI assistant to spot patterns or automate filtering of problematic domains.

Step-by-Step Process

1. Upload your list or integrate via API — Use bulk verification for large datasets or the real-time API for automated workflows. Both methods validate addresses at scale, checking syntax, domain existence, and mailbox validity.
2. Review the verification report — After processing, review the overview. Look for "catch-all" or "risky" verdicts. A catch-all domain accepts all addresses, even invalid ones, which means delivery failure or spam flagging is likely. This signals a high-risk domain that should be excluded.
3. Filter out or flag catch-all domains — Manually remove these from your campaign list or tag them for later review. This step prevents sending to non-functional or low-quality addresses, reducing bounce rates and protecting deliverability.
4. Use the in-app AI assistant — Select your list and let the AI analyze patterns. It can identify domains with recurring catch-all behavior, detect role-based or disposable domains, and suggest clean-up rules. This helps build consistent filtering criteria across future campaigns.

Why This Matters

Catch-all domains are a hidden source of failed deliveries. They don’t reject invalid addresses, making them useless for engagement. Sending to them can hurt your sender reputation over time — an issue confirmed by Spamhaus, which lists high-volume delivery to catch-all domains as a red flag for abuse.

Let’s be clear: verifying emails isn’t just about syntax. You’re validating inbox existence, domain health, and delivery intent. Emaillistchecker.io’s 98.9% accuracy means you’re not wasting effort on false positives. It checks for domain-level issues like greylisting, role accounts, and disposable email domains — all of which erode deliverability.

Once you’ve filtered out problematic domains, use inbox placement testing to validate if your campaign content hits the inbox. This ensures you’re not just cleaning lists — you’re improving real-world results.

Catch-All vs. Invalid vs. Risky: What the Verdicts Really Mean

When your email verification service labels an address as "catch-all," "invalid," or "risky," it's not just choosing labels—it's telling you how email servers treat that address. "Invalid" means the server outright rejects it. "Catch-all" means it accepts everything, which opens your list to spam traps and invalid sends. "Risky" flags accounts that may be role-based, temporary, or misconfigured. These verdicts define deliverability risk and sender reputation.

Understanding the Verdicts

Let’s break down what each result actually means in practice. Not all rejections are equal—and treating them as such wastes time and harms deliverability.

These distinctions matter. For example, RFC 5321 specifies how SMTP servers respond to mail delivery attempts, and the SMTP standard defines the 5xx error codes that confirm invalidity. A 550 response is definitive. A 250 response from a catch-all system is not proof of deliverability—it’s evidence of lax filtering.

Why Catch-All Detection Matters

Catch-all domains are a common deliverability time bomb. They don’t reject mail, so every email you send to a nonexistent user still succeeds on the server side. But that’s not a win—email providers like Gmail and Outlook track this behavior and may flag your sender as suspicious. According to research from Return Path and MxToolbox, high rates of undeliverable but accepted mail correlate with poor inbox placement.

If you're processing hundreds of emails, relying on basic syntax checks is ineffective. You need real-time validation with precise verdicts. Our bulk verification checks each address against current DNS, SMTP, and reputation systems—then returns a verdict with a 98.9% accuracy rate. For automations, the API allows you to integrate verification into your workflow on demand.

Pro Tip: Pair LLM Insights with Real-World Verification Data

You can use an LLM to classify catch-all emails faster, but only if you validate its output with real SMTP and DNS checks. Relying solely on language models risks false positives — especially with role accounts or typo domains. The best results come from using the LLM as a diagnostic filter, then grounding its guesses in actual verification data from tools like EmailListChecker.io’s bulk verification engine.

Let the LLM Guide the Process, Not Replace It

Think of the LLM prompt template not as a final verdict, but as a way to surface patterns in a list — like flagging addresses that follow a catch-all naming convention (e.g., [email protected], [email protected]). These are red flags, but not definitive proof. You still need to check if the domain actually accepts mail at that address using real SMTP connections or MX record analysis.

For example, a domain might be configured to accept mail for all addresses (a catch-all), but still reject delivery due to content filtering or greylisting. Tools like EmailListChecker.io use real-time SMTP validation to catch these cases, which an LLM alone cannot detect. The LLM identifies the possibility; real-world data confirms it.

Close the Loop with Feedback

When you run a list through both the LLM and actual verification, store the outcomes. Use the verified data — the final "valid"/"invalid"/"catch-all" labels — to retrain or refine your prompt template. This feedback loop improves accuracy over time.

Many platforms, including [RFC 5321](https://www.rfc-editor.org/rfc/rfc5321), define how mail servers should handle unknown recipients, but real-world behavior varies. A system that learns from actual delivery results adapts better than one that relies only on pattern matching.

Pairing AI insights with real verification isn’t just smart — it’s necessary. You can start with a quick test using the bulk verification tool to validate your approach, then scale with the API for automation. If you're building a list from scratch, the email finder ensures you begin with accurate records. And when you're ready to test real-world delivery, try our inbox placement reports.

Final takeaway: Automation beats manual prompt engineering

A well-crafted prompt template can help surface patterns in catch-all email behavior, but it cannot replace systematic verification. Manual analysis is slow, inconsistent, and prone to error — especially at scale.

Tools like Emaillistchecker.io automate the detection of catch-alls, disposable addresses, role accounts, and other deliverability risks with 98.9% accuracy. This frees your team from infrastructure minutiae and lets you focus on strategy.

Use the prompt template to deepen your understanding of email systems, not to substitute verified data. The real efficiency gain comes from integrating reliable verification into your workflow.

Sources

• Catch-all addresses made up 9% of all emails checked in 2025 — over 1 billion addresses that can look valid but still bounce and damage sender reputation. — ZeroBounce Email List Decay Report (2025)
• A 2025 list quality analysis found 11.7% of emails are invalid and another 7.9% are risky (spam traps, disposable addresses), meaning 19.6% of a typical list can damage sender reputation. — Apollo.io sender reputation guide (2025)

Keep reading

• Free email checker tools: syntax, MX, SMTP, disposable and catch-all checks (complete guide)
• Best Email Finder for SDRs and Outbound Sales Teams in 2026
• High Accuracy Email Checker for Coaching Niche Audiences
• Email Checker That Identifies Travel-Related Disposable Domains
• Syntax and MX Pre-Checks Before Paying for Full Verification

Ready to put this into practice? Emaillistchecker.io verifies emails with 98.9% accuracy — start with 100 free verifications.

Frequently asked questions

Can an LLM reliably detect catch-all domains without actual SMTP checks?

No. An LLM alone cannot verify email infrastructure. It requires structured input from real SMTP responses and DNS data to make accurate predictions.

What’s the difference between a catch-all and a disposable email?

A catch-all accepts all emails for a domain, regardless of user. A disposable email service provides temporary addresses and often uses a single inbox for all users.

How accurate is catch-all detection in Emaillistchecker.io?

Our system achieves 98.9% accuracy in email verification, including catch-all detection, by combining real-time SMTP checks and behavioral analysis.

Can catch-all domains still receive emails?

Yes. They accept all incoming messages, even for non-existent users, which undermines the reliability of individual email addresses.

Are all role accounts catch-all?

No. Role accounts (e.g., sales@, info@) are valid addresses but may not be personal or active. Catch-all refers to a domain-wide acceptance policy.

How can I test my LLM prompt before using it in production?

Use a small, labeled dataset of known catch-all and non-catch-all domains to test output consistency and confidence scores.

What happens if I send to a catch-all address?

The message will deliver, but the specific recipient may not be notified. This increases spam risk and harms sender reputation if overused.

Can I improve the accuracy of an LLM prompt over time?

Yes. By feeding it real verification results and refining the input structure, your prompt can evolve to improve context-aware classification.

Does Emaillistchecker.io use LLMs for classification?

We use AI in our in-app assistant for insights and analysis, but core verification relies on real-time SMTP and DNS checks, not generative LLMs.

Is catch-all detection useful in cold outreach?

Yes. Removing catch-all domains ensures emails go to real people, reducing bounces and improving engagement rates over time.

Why do some domains appear as 'risky' in verification results?

Risk flags highlight unusual behavior—such as inconsistent SMTP responses, role account patterns, or catch-all-like behavior—worth reviewing before sending.

Can I integrate Emaillistchecker.io with my email tool?

Yes. We support integrations with Mailchimp, SendGrid, HubSpot, and Klaviyo, allowing verified lists to flow directly into your campaigns.