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

# Create a dataset

> Four ways to build one — from traces, from clusters, from files, from scratch.

## From production traces

The most common source. In Traces, filter to the runs you want (see [Search & filter](/reliable/traces/search)), then **Add to dataset**.

Two flavors:

* **Trace-as-example** — the full trace becomes the example. Input = original input, expected output = the answer that was given (marked "capture as-is").
* **Corrected-example** — you edit the expected output to what the answer should have been.

Bulk: filter to hundreds of traces, click **Add all to dataset**. Nora asks whether to include corrections (if any exist as feedback) or capture as-is.

## From a signal cluster

Every cluster represents failing patterns. Right-click any cluster → **Save as dataset**:

* Each signal's trace becomes an example.
* If the signal has a text correction, that becomes the expected output.
* Otherwise, mark as "needs review" — you'll annotate before running simulations.

This is often the best pattern: the failures *are* the tests you want, and Nora already grouped them for you.

## From a file

Upload a CSV or JSONL:

**CSV**:

```csv theme={null}
input,expected_output,tags
"How do I reset my password?","Go to Settings → Security → Reset Password","auth,customer"
"When is my invoice due?","Invoices are due on the first of each month","billing,customer"
```

**JSONL** (more flexible — supports structured inputs and outputs):

```jsonl theme={null}
{"input":{"question":"reset password"},"expected":{"answer":"..."},"tags":["auth"]}
```

Files are the way to import datasets from external QA systems.

## From scratch

**Datasets → New → Add example**. Type or paste input, type expected output, tag. Fastest for a handful of hand-crafted examples to bootstrap testing.

## Multi-turn examples

Some Flows are conversational. Multi-turn examples are supported:

* Each example is a **conversation** (list of turns).
* Each turn has expected inputs and expected outputs.
* Simulation replays the conversation and grades each turn.

Import format:

```jsonl theme={null}
{"conversation":[
  {"role":"user","content":"..."},
  {"role":"assistant","content":"expected answer"},
  {"role":"user","content":"follow-up"},
  {"role":"assistant","content":"expected answer 2"}
]}
```

## Tags and grouping

Every example can carry tags. Common uses:

* **Feature** — which product feature this exercises.
* **Difficulty** — easy / medium / hard.
* **Source** — where the example came from (`prod`, `synthetic`, `hand-crafted`).
* **Language** — for multilingual coverage.

Simulation can be run against a tag subset — "run the customer-facing hard cases only".

## Metadata per example

Beyond input/expected/tags, examples can carry:

* **Scope** — for testing per-tenant/per-user behavior.
* **Attached context** — session memory, prior turns, feature flags.
* **Notes** — free-text for reviewers.

Match production runtime as closely as possible so simulation results reflect real behavior.

## Editing after import

Every example is editable in place. Bulk edit tags or notes via multi-select. Delete individual examples anytime.