We've been running Hailuo's classification module on our customer support ticket stream for about three months now, and we're observing a persistent and problematic pattern of misclassification. Specifically, tickets concerning "billing discrepancies" are routinely being labeled as "feature requests," while "technical outage" reports are often incorrectly tagged as "general inquiry." This is causing significant routing delays and operational friction, as tickets require manual re-triaging before reaching the appropriate engineering or finance teams.
Given the architectural promise of the platform, I suspect the issue lies not in the base model's capabilities but in our prompt construction and the contextual framing of the input data. Our current ingestion pipeline pre-processes the raw ticket text (subject + body) and sends it to the classification endpoint with a prompt that is likely too simplistic.
Our current implementation looks roughly like this:
```yaml
classification_prompt: |
Categorize the following customer support ticket into one of these categories:
[billing, feature_request, technical_outage, general_inquiry, bug_report].
Ticket: {{ticket_text}}
Category:
```
I hypothesize several potential failure modes inherent in this approach:
* **Lack of Definitional Context:** The model is operating on an implicit, and likely incorrect, understanding of our internal category boundaries. What we define as a "billing discrepancy" (e.g., "I was charged twice for my subscription renewal") might semantically overlap with a feature request (e.g., "I need a feature to see more detailed invoices") from a pure language model perspective.
* **Absence of Examples:** We are relying on zero-shot classification without providing clear, canonical examples (few-shot learning) to ground the model's decision-making in our specific domain.
* **Instruction Placement and Format:** The instruction is placed before the ticket text, which may not be optimal for the model's attention mechanisms. Furthermore, requiring a single token output (the category name) might be conflicting with the model's natural text completion tendencies.
I am considering a few structural modifications to the prompt strategy and would appreciate insights from others who have tackled similar granular classification problems:
* Implementing a **few-shot prompt** with 2-3 unambiguous examples per category, clearly demonstrating the distinguishing characteristics.
* Reframing the task as a **multi-label classification** or including a confidence threshold, allowing the system to flag low-confidence predictions for human review, rather than forcing a potentially incorrect single label.
* Adding **explicit category definitions** within the prompt itself, creating a stronger contextual boundary. For instance, defining "billing" as "issues related to charges, invoices, refunds, or payment methods" and "feature_request" as "suggestions for new functionality or changes to existing features."
* Experimenting with **output formatting constraints**, such as requiring the model to output only the exact category keyword from a provided list, or using a structured JSON output format to reduce ambiguity.
Has anyone conducted systematic A/B testing on Hailuo's classification prompts for similar use cases? I'm particularly interested in empirical results regarding the impact of example count versus definition clarity on accuracy for a constrained set of 5-10 categories. Additionally, any data on the performance trade-offs between a single, complex prompt versus a two-stage classification pipeline (e.g., first: urgency/impact, second: specific category) would be highly valuable.
Latency is the enemy
Yeah, that prompt is way too open-ended. You're asking it to be a mind reader. Billing and feature requests can sometimes share vocabulary like "I want," "add," or "change," and without concrete guardrails, the model will guess based on statistical likelihood, not your operational needs.
Add explicit, rule-based definitions to your prompt, right up front. Force the model to evaluate against criteria, not just perform fuzzy matching. Try something like:
```
Categorize the ticket using the exact rules below.
- billing: Mentions charges, invoices, payments, overcharges, refunds. Do NOT use if it suggests new functionality.
- feature_request: Explicitly asks for a new capability or a change to existing functionality. Not for correcting perceived errors.
- technical_outage: Reports a complete service disruption or system-wide failure. Keywords: down, cannot access, broken, outage.
- general_inquiry: A question about how to use an existing feature or about company policy.
- bug_report: Describes incorrect behavior of an existing feature.
Ticket: {{ticket_text}}
Category:
```
You also need a feedback loop. Export the misclassified tickets weekly, analyze the common phrases that triggered the wrong label, and bake those as negative examples into your prompt definitions. Prompt engineering isn't a one-shot task, it's ongoing tuning.
That prompt is basically useless. It's like handing someone a list of five folders and telling them to "file this," with no instructions on what belongs in each folder.
The user423 suggestion to add rules is correct, but it's only half the battle. You also need to inject *negative examples* into your prompt. The model needs to see what a category is NOT.
Your biggest leak is billing -> feature request. You have to explicitly block that path. Try appending a "Critical Instructions" section to your prompt that looks like this:
```
Critical Instructions:
- If a user is complaining about an amount on their invoice, it is ALWAYS 'billing', even if they use the word "want" or "request."
- A 'technical_outage' means the user cannot use the service at all. If they can log in but a feature is broken, it is a 'bug_report'.
- A 'general_inquiry' is a catch-all for questions that do not fit any other rule.
```
Then, before you go all-in on prompt engineering, check your pipeline. Is `{{ticket_text}}` getting truncated? Are you stripping formatting that provides context, like prior ticket references or system-generated error codes? I've seen classification fail because the pre-processing step removed the string "ERROR-500" which was the only clear signal.
Automate everything. Twice.
Your prompt is basically a fire hose into a bucket. You're giving it text and a label list, but no decision framework.
The suggestions to add rules are correct, but they miss a critical piece: you need to define the *input scope* first. Your pre-processed ticket text might be stripping crucial metadata like ticket source or subject line formatting that a human uses to triage. Are you including the ticket subject? The priority flag? The first agent's internal note? If not, you're handicapping it before it even starts.
Also, try reframing the prompt as a multi-step evaluation. Don't ask for a category. Ask a series of yes/no questions based on your most common misclassifications.
```text
Follow this decision tree:
1. Does the ticket report a complete service interruption or inability to log in? If yes, output 'technical_outage'.
2. Does the ticket question a charge, invoice, or payment amount? If yes, output 'billing'.
3. Does the ticket explicitly ask for a new feature or a change to current functionality? If yes, output 'feature_request'.
...
Ticket: {{ticket_text}}
```
This forces sequential logic over pattern matching. You should also run a batch of your mislabeled tickets through a playground with this structure and see if it sticks.
Build once, deploy everywhere
The multi-step decision tree is solid, but you're assuming the model will actually follow the sequence. Without a strong instruction to evaluate steps *in order*, these models often read the whole list first and jump to a pattern match.
You need to lock the logic down. Prefix it with "You MUST evaluate these questions sequentially. Do not proceed to question 2 unless the answer to question 1 is 'no'."
Also, batch testing is good, but you need to measure latency per-step. That tree adds token overhead; make sure the added classification time doesn't break your SLA.