> ## Documentation Index > Fetch the complete documentation index at: https://docs.oumi.ai/llms.txt > Use this file to discover all available pages before exploring further. # EXAMPLES > Explore a curated set of prompts that demonstrate Oumi’s capabilities By interacting with the Oumi Agent, you can quickly create, evaluate, and train models with precision, regardless of where you are in the machine learning lifecycle. The following are just a few examples that highlight some of the tasks you can accomplish using simple natural language prompts. Check out the [Prompt Library](/reference/prompts) for more examples of what you can do with the Oumi Agent. *** ## TRAIN MODELS Fine-tune a model to automatically categorize customer support questions by topic and urgency. ```prompt theme={null} Fine-tune a model to classify customer support questions by topic and urgency ```

Fine-tune Qwen to identify the intent behind incoming customer support messages. ```prompt theme={null} Fine-tune Qwen to detect the intent behind incoming customer support messages ```

Train a compact model for fraud detection using on-policy distillation techniques. ```prompt theme={null} Train a compact model by distilling a large fraud detection model’s responses into a smaller model using on policy distillation. ```

*** ## BUILD & ANALYZE DATASETS Create a structured dataset of customer support tickets labeled by both urgency and topic to support classification tasks. ```prompt theme={null} Create a labeled dataset of customer support tickets categorized by urgency and topic ```

Create 500 question-answer pairs from a product documentation PDF for training or evaluation use. ```prompt theme={null} Generate 500 question-answer pairs from a product documentation PDF ```

Increase dataset size and diversity by generating new samples that follow the same style and format. ```prompt theme={null} Expand my dataset by generating new samples that match the style and format of my existing examples ```

Improve evaluation robustness by generating additional samples consistent with your existing dataset. ```prompt theme={null} Expand my dataset by generating additional samples consistent with my existing dataset to improve my evaluation ```

Analyze your dataset and generate new tasks with detailed solutions for addressing missing coverage. ```prompt theme={null} Analyze my coding dataset and generate additional tasks with step-by-step solution breakdowns to fill gaps ```

*** ## EVALUATE MODELS Define evaluators that score model outputs based on key criteria like helpfulness and accuracy. ```prompt theme={null} Create evaluators to score my model's responses for helpfulness and accuracy ```

Build a targeted evaluator to assess how well your model performs on customer support interactions. ```prompt theme={null} Create an evaluator to measure my model's response quality on customer support tickets ```

Run evaluations to uncover common weaknesses and failure modes in your model’s responses. ```prompt theme={null} Evaluate Qwen on a customer support dataset and surface the most common failure patterns ```

Evaluate model performance on sentiment classification using real-world product review data. ```prompt theme={null} Test Qwen on a sentiment analysis task using customer food product reviews ```

*** ## WHAT'S NEXT Build your first model by diving into the [Quickstart](/guides/quickstart) and building your first custom machine learning model in Oumi.