> ## Documentation Index
> Fetch the complete documentation index at: https://docs.overmindlab.ai/llms.txt
> Use this file to discover all available pages before exploring further.

# Tutorials

> Learn dataset-to-model workflows, tracing and evaluation through your coding agent.

Each tutorial starts with one prompt and ends with something you can inspect. Your agent handles the MCP calls, local files and CLI commands. Start with the [quick-start instructions](https://console.overmindlab.ai/setup.md) and select a separate tutorial project. The [connection guide](/how-to/connect.md) explains how to verify access.

Read [https://console.overmindlab.ai/setup.md](https://console.overmindlab.ai/setup.md) and follow the instructions to set up Overmind in my coding agent.

Select your project when the agent shows the available choices. It carries that context through the conversation.

| Lesson | You ask for | You will inspect |
| - | - | - |
| [Train a decision model](/tutorials/first-dataset.md) | Prepare data, train a model and benchmark it | A trained model, baseline comparison and example mistakes |
| [Fine-tune a language model](/tutorials/fine-tune-language-model.md) | Prepare examples, fine-tune and benchmark text responses | Base-model and fine-tuned outputs, scores and example mistakes |
| [Record a trace](/tutorials/first-trace.md) | Write and run one local tracing example | One trace with a tool call and its result |
| [Run an evaluation](/tutorials/first-evaluation.md) | Check recorded fraud labels | Valid answers and a deliberate vocabulary failure |

Both model-training tutorials and the evaluation tutorial use the same [fraud example dataset](/reference/example-dataset.md). The trace lesson uses a local function and needs no external data. You can take it independently.

Both model-training tutorials include training and benchmark inference. The trace example application makes no model calls, but enabled platform scoring may; the label-format evaluation does not train a model, although the platform may generate explanations for low scores. Your coding agent uses its own provider. Each tutorial states the work it starts.


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