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+---
+title: Prompts examples
+short: Examples
+tier: enterprise
+type: guide
+order: 0
+order_enterprise: 236
+meta_title: Prompts examples
+meta_description: Example use cases for Prompts
+section: Prompts
+date: 2025-01-15 12:11:22
+---
+
+
+## Autolabel image captions
+
+This example demonstrates how to set up Prompts to predict image captions.
+
+1. [Create a new label studio project](setup_project) by importing image data via [cloud storage](storage).
+
+!!! note
+ Prompts does not currently support image data uploaded as raw images. Only image references (HTTP URIs to images) or images imported via cloud storage are supported.
+
+!!! info Tip
+ If you’d like to, you can generate a dataset to test the process using [https://data.heartex.net](https://data.heartex.net).
+
+2. Create a [label config](setup) for image captioning, for example:
+
+```xml
+<View>
+ <Image name="image" value="$image"/>
+ <Header value="Describe the image:"/>
+ <TextArea name="caption" toName="image" placeholder="Enter description here..."
+ rows="5" maxSubmissions="1"/>
+</View>
+```
+3. Navigate to **Prompts** from the sidebar, and [create a prompt](prompts_create) for the project
+
+!!! note
+ If you have not yet set up API the keys you want to use, do that now: [API keys](prompts_create#Model-provider-API-keys).
+
+4. Add the instruction you’d like to provide the LLM to caption your images. For example:
+
+ *Explain the contents of the following image: `{image}`*
+
+!!! note
+ Ensure you include `{image}` in your instructions. Click `image` above the instruction field to insert it.
+
+
+
+!!! info Tip
+ You can also automatically generate the instructions using the [**Enhance Prompt** action](prompts_draft#Enhance-prompt). Before you can use this action, you must at least add the variable name `{image}` and then click **Save**.
+
+
+
+5. Run the prompt. View predictions to accept or correct.
+
+ You can [read more about evaluation metrics](prompts_draft#Evaluation-results) and ways to assess your prompt performance.
+
+!!! info Tip
+ Use the drop-down menu above the results field to change the subset of data being used (e.g. only data with Ground Truth annotations, or a small sample of records).
+
+
+
+6. Accept the [predictions as annotations](prompts_predictions#Create-annotations-from-predictions).
+
+
+## Evaluate LLM outputs for toxicity
+
+This example demonstrates how to set up Prompts to evaluate if the LLM-generated output text is classified as harmful, offensive, or inappropriate.
+
+1. [Create a new label studio project](setup_project) by importing text data of LLM-generated outputs.
+
+ You can use this preprocessed sample of the [jigsaw_toxicity](https://huggingface.co/datasets/tasksource/jigsaw_toxicity) dataset as an example. See [the appendix](#Appendix-Generate-dataset) for how this was generated.
+2. Create a [label config](setup) for toxicity detection, for example:
+
+```xml
+<View>
+ <Header value="Comment" />
+ <Text name="comment" value="$comment_text"/>
+
+ <Header value="Toxic" size="3"/>
+ <Choices name="toxic" toName="comment" choice="single" showInline="true">
+ <Choice value="Yes" alias="1"/>
+ <Choice value="No" alias="0"/>
+ </Choices>
+ <Header value="Severely Toxic" size="3"/>
+ <Choices name="severe_toxic" toName="comment" choice="single" showInline="true">
+ <Choice value="Yes" alias="1"/>
+ <Choice value="No" alias="0"/>
+ </Choices>
+ <Header value="Insult" size="3"/>
+ <Choices name="insult" toName="comment" choice="single" showInline="true">
+ <Choice value="Yes" alias="1"/>
+ <Choice value="No" alias="0"/>
+ </Choices>
+ <Header value="Threat" size="3"/>
+ <Choices name="threat" toName="comment" choice="single" showInline="true">
+ <Choice value="Yes" alias="1"/>
+ <Choice value="No" alias="0"/>
+ </Choices>
+ <Header value="Obscene" size="3"/>
+ <Choices name="obscene" toName="comment" choice="single" showInline="true">
+ <Choice value="Yes" alias="1"/>
+ <Choice value="No" alias="0"/>
+ </Choices>
+ <Header value="Identity Hate" size="3"/>
+ <Choices name="identity_hate" toName="comment" choice="single" showInline="true">
+ <Choice value="Yes" alias="1"/>
+ <Choice value="No" alias="0"/>
+ </Choices>
+
+ <Header value="Reasoning" size="3"/>
+ <TextArea name="reasoning" toName="comment" editable="true" placeholder="Provide reasoning for your choices here..."/>
+</View>
+```
+
+3. Navigate to **Prompts** from the sidebar, and [create a prompt](prompts_create) for the project
+
+!!! note
+ If you have not yet set up API the keys you want to use, do that now: [API keys](prompts_create#Model-provider-API-keys).
+
+4. Add the instruction you’d like to provide the LLM to best evaluate the text. For example:
+
+ *Determine whether the following text falls into any of the following categories (for each, provide a "0" for False and "1" for True):*
+
+ *toxic, severe_toxic, insult, threat, obscene, and identity_hate.*
+
+ *Comment:*
+
+ *`{comment_text}`*
+
+
+!!! note
+ Ensure you include `{comment_text}` in your instructions. Click `comment_text` above the instruction field to insert it.
+
+
+
+!!! info Tip
+ You can also automatically generate the instructions using the [**Enhance Prompt** action](prompts_draft#Enhance-prompt). Before you can use this action, you must at least add the variable name `{comment_text}` and then click **Save**.
+
+
+
+5. Run the prompt. View predictions to accept or correct.
+
+ You can [read more about evaluation metrics](prompts_draft#Evaluation-results) and ways to assess your prompt performance.
+
+!!! info Tip
+ Use the drop-down menu above the results field to change the subset of data being used (e.g. only data with Ground Truth annotations, or a small sample of records).
+
+
+
+6. Accept the [predictions as annotations](prompts_predictions#Create-annotations-from-predictions).
+
+### Appendix: Generate dataset
+
+Download the jigsaw_toxicity dataset, then downsample/format using the following script (modify the `INPUT_PATH` and `OUTPUT_PATH` to suit your needs):
+
+```python
+import pandas as pd
+import json
+
+
+def gen_task(row):
+ labels = [
+ {
+ "from_name": field,
+ "to_name": "comment",
+ "type": "choices",
+ "value": {"choices": [str(int(row._asdict()[field]))]},
+ }
+ for field in [
+ "toxic",
+ "severe_toxic",
+ "insult",
+ "threat",
+ "obscene",
+ "identity_hate",
+ ]
+ ]
+ return {
+ "data": {"comment_text": row.comment_text},
+ "annotations": [
+ {
+ "result": labels,
+ "ground_truth": True,
+ "was_cancelled": False,
+ }
+ ],
+ }
+
+
+INPUT_PATH = "/Users/pakelley/Downloads/Jigsaw Toxicity Train.csv"
+OUTPUT_PATH = "/Users/pakelley/Downloads/toxicity-sample-ls-format.json"
+
+df = pd.read_csv(INPUT_PATH)
+sample = df.sample(n=100)
+label_studio_tasks = [gen_task(row) for row in sample.itertuples()]
+with open(OUTPUT_PATH, "w") as f:
+ json.dump(label_studio_tasks, f)
+```
+
+If you choose to, you could also easily change how many records to use (or use the entire dataset by removing the sample step).
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