---
title: Adaptive Data App Walkthrough | Adaption
description: Import a dataset, map its columns, configure adaptation, launch a run, and review the results in the Adaption app.
---

## Before you start

Sign in at [adaptionlabs.ai/app](https://adaptionlabs.ai/app). You do not need an API key for this app walkthrough.

Adaptive Data can build:

- **Instruction datasets**, which pair prompts with expected completions for supervised fine-tuning.
- **Preference pairs**, which compare two completions and identify the preferred one.

## Step 1 | Bring in your data

[Create a dataset](/adaptive-data/create-a-dataset/index.md) in the app from one of these sources:

- **Local file:** CSV, JSON, JSONL, Parquet, PDF, DOCX, PPTX, XLSX, HTML, ZIP, or TXT.
- **Hugging Face:** enter a dataset URL and select the files to import. If the dataset is gated, request access on Hugging Face before importing it.
- **Kaggle:** enter the dataset page URL.

Document-style sources such as PDFs, scans, slide decks, and email threads trigger an additional extraction and splitting flow. See [Processing unstructured documents](/tutorials/processing-unstructured-documents/index.md).

Adaptive Data does not currently support non-text tabular data such as financial time series.

## Step 2 | Expand the dataset

In the [**Expansion** step](/adaptive-data/configure-adaptive-data#expand-across-languages-and-locales/index.md), optionally increase the size of the dataset:

- **Translate** creates translated versions of existing rows. The app supports up to 242 languages; see the [supported language list](https://docs.google.com/spreadsheets/d/17NTJw8MF04B_zpNLT72V1bXbQk-VJOxuYOah1w7YboY/edit?usp=sharing).
- **Localize** adapts rows for both a language and a country, including locale-specific wording rather than direct translation alone.

Skip expansion when you need to preserve the current dataset size.

## Step 3 | Map your columns

[Assign each source column a role](/adaptive-data/select-columns/index.md):

- **Prompt:** the question, instruction, or task in each row.
- **Context:** background material needed to answer the prompt. You can select multiple context columns, including images.
- **Completion:** the expected response.
- **Chat:** a multi-turn conversation or dialogue history.

Every run needs at least a prompt or a completion. Adaptive Data can generate the missing role.

- No prompt column: select **I don’t have prompt**, then either let Adaption create one per row or select **Write universal prompt**.
- No completion column: select **I don’t have a completion column** to generate one.

For example, a document-extraction dataset could use `document_body` as context and this universal prompt:

> Extract the customer name, contract start date, renewal date, and total contract value. Return valid JSON with the keys `customer_name`, `start_date`, `renewal_date`, and `contract_value`.

Make sure every column needed to answer the prompt is mapped as context. The column-selection guide includes detailed mapping patterns for [image columns](/adaptive-data/select-columns#map-an-image-column/index.md).

## Step 4 | Evaluate data quality

The [**Data Evaluation** step](/adaptive-data/evaluate-dataset-quality/index.md) reports:

- A letter grade and quality score from 0 to 10.
- A percentile relative to other datasets in the detected domain.
- Detected domain, language, and tone.

Use this baseline to compare the source dataset with the adapted result after the run.

## Step 5 | Choose recipes

[Select only the recipes](/adaptive-data/configure-adaptive-data#apply-recipes/index.md) that fit your goal:

- **Prompt Deduplication** removes duplicate prompts so repeated examples do not dominate the dataset.
- **Prompt Rephrasing** rewrites prompts for clarity and alignment with the task.
- **Reasoning Traces** adds step-by-step reasoning alongside final outputs for supported workflows.
- **Hallucination Mitigation** applies grounding and verification to reduce unsupported completions.
- **Freeform Brand Voice** steers generated data toward a particular personality, tone, and style.

If prompts must remain byte-for-byte unchanged, disable **Prompt Rephrasing** and **Metadata Injection**.

## Step 6 | Configure Blueprint

If you answer **Yes** to **Do you want to add brand guidelines or global constraints?**, the app opens [Blueprint and its brand controls](/adaptive-data/configure-adaptive-data#configure-brand-controls/index.md).

Enter a system-level instruction directly, or upload brand guidelines, a system prompt, or policies for safety, tone, and length. For example:

> Write concise customer-support answers in British English. Use a warm, direct tone. Never recommend third-party products, and do not make claims that are not supported by the supplied context.

Blueprint also provides structured controls:

- **Length:** Detailed, Extensive, Minimal, or Concise.
- **Safety Classification:** Sexual, Self-harm, Hate, Harassment, and Violence.
- **Hallucination mitigation:** reduces unsupported content in generated completions.

These controls apply to every adapted row, so use them for true global requirements rather than row-specific guidance.

## Step 7 | Review and launch

The **Summary** screen shows:

- A [**Reduce Rows** slider](/adaptive-data/configure-adaptive-data#limit-rows-and-estimate-a-run/index.md) for processing fewer rows.
- Your available credits.
- The exact credit cost of the configured job.

Review the estimate, then launch the run. The dashboard can show these states:

- **Job launched:** submitted or queued.
- **Running:** actively processing.
- **Job completed:** results and a quality score are available.
- **Job failed:** processing stopped before completion.

Large imports can spend several hours in the queue. If a job appears stuck:

1. Compare the elapsed time with the displayed estimate and allow for queue time.
2. Confirm the dataset uses supported prompt/completion columns or OpenAI-style chat data.
3. Retry a failed job once, especially if it reached 100% before failing.
4. If it remains stuck or fails again, [contact support](/resources/support/index.md) and include the dataset ID.

## Step 8 | Review results

When processing finishes, open the dataset and select the **View** tab. Each row shows source and adapted data side by side, including original and enhanced prompts, original and enhanced completions, and inline context.

Compare the before-and-after:

- Quality score, from 0 to 10.
- Letter grade.
- Domain percentile, from 0 to 100.
- Individual source and adapted rows.

Row count can decrease because prompt deduplication removes duplicate examples.

## Common issues

- **Job launched for hours:** queue time may not be included in the estimate. Wait beyond the displayed estimate before escalating.
- **Pending Hugging Face or Kaggle image import:** if a multimodal import does not progress, [contact support](/resources/support/index.md) with the dataset ID.
- **Processing failed for a large JSONL file:** retry once; if it fails again, contact support with the dataset ID.
- **Dataset rejected for format:** reshape it into prompt/completion columns or an OpenAI-style chat column, then repeat [column mapping](#step-3--map-your-columns).
- **Prompts changed unexpectedly:** disable **Prompt Rephrasing** and **Metadata Injection** for strict prompt fidelity.

## Next steps

- [Work with image columns](/adaptive-data/select-columns#map-an-image-column/index.md)
- [Configure Adaptive Data in detail](/adaptive-data/configure-adaptive-data/index.md)
- [Process unstructured documents](/tutorials/processing-unstructured-documents/index.md)
- Use the [API reference](/api/python/index.md) when you are ready to automate the same workflow.