# Text Generation

For text generation tasks, you can upload a dataset with prompts and expected completions. Each prompt should provide context and cues for the model to generate a coherent and relevant completion.

Here is an example of what your dataset might look like:<br>

| Prompt                                                                         | Completion                                                                                                                                                               |
| ------------------------------------------------------------------------------ | ------------------------------------------------------------------------------------------------------------------------------------------------------------------------ |
| Name: Women's T-Shirt ; Material: Cotton ; Color: Blue ; Size: S ; Price: $19  | A soft and comfortable women's t-shirt made from high-quality cotton material, featuring a classic blue color that's perfect for everyday wear. Available in size S.     |
| Name: Men's Hoodie ; Material: Fleece ; Color: Gray ; Size: M ; Price: $29     | A warm and cozy men's hoodie made from premium fleece material, featuring a stylish gray color that's perfect for casual wear. Available in size M.                      |
| Name: Women's Dress ; Material: Silk ; Color: Red ; Size: L ; Price: $99       | A luxurious and elegant women's dress made from high-quality silk material, featuring a stunning red color that's sure to turn heads. Available in size L.               |
| Name: Men's Sneakers ; Material: Leather ; Color: Black ; Size: 9 ; Price: $49 | A stylish and comfortable pair of men's sneakers made from premium leather material, featuring a sleek black color that's perfect for any occasion. Available in size 9. |
| Name: Women's Jeans ; Material: Denim ; Color: Indigo ; Size: 27 ; Price: $39  | A classic pair of women's jeans made from durable denim material, featuring a versatile indigo color that's perfect for everyday wear. Available in size 27.             |


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# Agent Instructions: Querying This Documentation

If you need additional information that is not directly available in this page, you can query the documentation dynamically by asking a question.

Perform an HTTP GET request on the current page URL with the `ask` query parameter:

```
GET https://docs.texti.ai/data-labeling/text-generation.md?ask=<question>
```

The question should be specific, self-contained, and written in natural language.
The response will contain a direct answer to the question and relevant excerpts and sources from the documentation.

Use this mechanism when the answer is not explicitly present in the current page, you need clarification or additional context, or you want to retrieve related documentation sections.