---
description: Save time labeling thousands of tickets. Learn a semi-automated workflow
  using GPT for zero-shot pre-labeling and Label Studio for efficient review.
---
# Labeling 10,000 Tickets Efficiently: Semi-Automated Labeling Strategies

Labeling thousands of support tickets manually is time-consuming and expensive. A **semi-automated
workflow** leverages large language models (LLMs) like GPT to **pre-label** tickets (using
zero-shot/few-shot prompts) and then uses human annotators to **review and correct** those labels.
This hybrid approach dramatically cuts annotation effort: for example, one case study found
GPT-generated “pre-annotations” *“good enough to help us speed up the labeling process”*. In
practice, *minimal labels* from the model can reduce time and cost of annotation. In this article we
explain how to set up such a pipeline, show Python examples (using GPT via OpenRouter or OpenAI),
and discuss tools like Label Studio for review.

## Using GPT for Zero-Shot/Few-Shot Pre-Labeling

Modern LLMs can classify text with **zero or few examples**. In zero-shot labeling, the model
assigns categories without being explicitly trained on ticket data. As one tutorial puts it:
*“Zero-shot learning allows models to classify new instances without labeled examples”*. In
practice, you craft a prompt instructing GPT to tag a ticket. For example:

```text
Ticket: "Cannot login to account."
Classify this ticket into one of {Bug, Feature Request, Question}.
```

The model then replies with a label. Few-shot labeling adds a couple of examples in the prompt to
improve accuracy. This means we can generate initial labels **directly via the API** without any
model training.

> **Tip:** Use a structured prompt or ask for JSON output to make parsing easy. For example:
>
> ```
> Ticket: "Password reset email bounced."
> Respond in JSON like {"category": "..."}.
> ```
>
> This helps integrate the response into your pipeline.

## Automated Pre-Labeling with AI APIs

You can use AI APIs like OpenAI or OpenRouter to automatically pre-label tickets before human review. The process involves:

1. Loop through your ticket list
2. Send each ticket text to an AI model with a classification prompt
3. Store the predicted category as a pre-label
4. Human reviewers verify and correct the pre-labels

This approach significantly reduces manual labeling time while maintaining quality through human oversight. OpenRouter provides a unified API that works with multiple AI providers (OpenAI, Anthropic Claude, Google PaLM, etc.), allowing you to switch between models or use fallback options for high availability.

## Integrating Pre-Labels with Labeling Tools

Once GPT generates labels, the next step is **importing them into a labeling interface** for human
review. One popular open-source solution is [Label Studio](https://labelstud.io). Label Studio
supports importing model predictions as “pre-annotations” alongside the data. Annotators see the
suggested label and only need to correct mistakes, not label from scratch. In effect, the team
*“shifts from the time-intensive task of data labeling to the far more efficient process of
reviewing and refining the preliminary labels”*.

Label Studio even offers an ML backend: you can write a small server using the `LabelStudioMLBase`
class that calls GPT for each task. In their tutorial, Label Studio shows wrapping GPT-4 calls in
this class to return predictions on the fly. Alternatively, you can import a JSON file of
predictions. The required JSON format has a `data` field (the ticket text) and a `predictions`
array (containing each label). For example (simplified):

```json
[
    {
        "data": {
            "text": "User cannot login to account"
        },
        "predictions": [
            {
                "result": [
                    {
                        "value": {
                            "choices": [
                                {
                                    "text": "Bug"
                                }
                            ]
                        }
                    }
                ]
            }
        ]
    },
    {
        "data": {
            "text": "Add dark mode to settings"
        },
        "predictions": [
            {
                "result": [
                    {
                        "value": {
                            "choices": [
                                {
                                    "text": "Feature Request"
                                }
                            ]
                        }
                    }
                ]
            }
        ]
    }
]
```

After importing, Label Studio will display each ticket with the model’s label pre-filled. The
annotator’s job is to **review and correct**. This semi-automated workflow has been shown to work
well: a Kili Technology example demonstrated loading a GPT-pre-labeled dataset and noted *“we have
successfully pre-annotated our dataset”* and that this approach *“has the potential to save us a lot
of time”*. In practice, GPT’s accuracy on labeling might be \~80–90%, meaning humans correct only
the remaining 10–20%.

## Tools and Workflow Steps

To summarize, a typical semi-automated labeling pipeline looks like this:

* **Prepare the ticket dataset.** Export your 10,000 unlabeled tickets (e.g. as JSON or CSV).
* **Generate pre-labels via LLM.** Run code (like above) calling GPT-4 (or another model via
  OpenRouter) to classify each ticket. Save the responses.
* **Import predictions into a labeling tool.** Use Label Studio (or similar) to load tickets and
  associate each with the GPT-generated label (the “prediction”). Label Studio docs explain how to
  import predictions with your data.
* **Human review.** Annotators go through the tickets in Label Studio, accepting or correcting the
  labels. This is much faster than labeling from scratch. Label Studio’s interface highlights the
  model suggestion for each task, so the task becomes quick validation.
* **Export final labels.** Once reviewed, export the corrected annotations for model training or
  analytics.

Key public tools that support this approach include:

* **OpenRouter** – a unified LLM API gateway (openrouter.ai). It lets you easily switch between
  GPT-4, Anthropic Claude, Google PaLM, etc.. You can even specify a fallback list in one API call.
* **OpenAI API (GPT-4/3.5)** – the core engine for generating labels with zero/few-shot prompts.
* **Label Studio** – an open-source data labeling UI. It supports importing predictions and has an
  ML backend to call models.
* **Doccano** – a simpler open-source tool for text annotation (classification, NER, etc.). It does
  not have built-in LLM integration, but you can still use GPT offline to generate labels and load
  them as initial choices.
* **Snorkel/Programmatic Labeling** – for some rule-based or weak-supervision cases, tools like
  Snorkel can complement LLM labels, but modern LLMs often cover many cases out of the box.

## Dummy Ticket Data Example

To illustrate, here is some *dummy ticket data* you might work with:

```python
tickets = [
    {"id": 101, "text": "Error 500 when saving profile", "label": None},
    {"id": 102, "text": "How do I change my subscription plan?", "label": None},
    {"id": 103, "text": "Feature request: dark mode in settings", "label": None},
    {"id": 104, "text": "Application crashes on startup", "label": None},
]
```

You could feed each `ticket['text']` to GPT with a prompt like:

```text
Ticket: "Error 500 when saving profile."
Classify this issue as one of {Bug, Feature, Question}.
```

Suppose GPT returns `"Bug"`, `"Question"`, `"Feature"`, `"Bug"` respectively. After the loop,
`tickets` might be:

```python
[
    {'id': 101, 'text': 'Error 500 when saving profile', 'label': 'Bug'},
    {'id': 102, 'text': 'How do I change my subscription plan?', 'label': 'Question'},
    {'id': 103, 'text': 'Feature request: dark mode in settings', 'label': 'Feature'},
    {'id': 104, 'text': 'Application crashes on startup', 'label': 'Bug'},
]
```

These labels would then be loaded into the review interface. Even if some are wrong (e.g. GPT might
mislabel a tricky bug as a feature), the annotator only needs to *fix* them instead of starting from
scratch. Empirically, GPT-generated labels often reach \~80–90% accuracy, so reviewing is much
faster than full labeling.

## Results and Takeaways

The semi-automated approach scales well. In a large project, human annotators might only need to fix
a few hundred or thousand labels instead of 10,000. As the Kili tutorial observed after running GPT
pre-labels: *“Great! We have successfully pre-annotated our dataset. Looks like this solution has
the potential to save us a lot of time in future projects.”*. In other words, LLMs serve as a force
multiplier. Even though the model isn’t 100% correct, it **“speeds up the labeling process”** by
doing most of the work.

**Best practices:** Use a low temperature (e.g. 0.0–0.3) for consistent labels, and provide clear
instructions or a small list of examples. Monitor GPT’s mistakes: you may need to adjust prompts or
add a few shot examples for underperforming categories. Keep the prompt simple (e.g. “Classify the
ticket text into A, B, or C”). You can also batch multiple tickets in one API call if the model and
API allow it, to save cost. And always include human review – this ensures high quality and catches
any LLM errors or drift.

## Conclusion

Semi-automated labeling with GPT and tools like OpenRouter and Label Studio is a powerful strategy
for quickly labeling large text datasets. By **pre-labeling 10,000 tickets with an LLM and then
reviewing**, companies can jump-start their AI workflows with minimal initial data. This approach
cuts costs and time dramatically while still ensuring quality through human oversight. As one
implementation guide notes, shifting the workflow from *“data labeling”* to *“reviewing and
refining”* of LLM-generated labels *“significantly accelerates your workflow.”*. In short, combining
GPT-based pre-annotation with a friendly UI (Label Studio, Doccano, etc.) helps software/AI teams
label massive ticket datasets efficiently and accurately.

