# Quickstart Tutorial

## Get started with no-code tools

If you're new to working with public web data or just looking for a faster way to access it, Coresignal's no-code tools offer a simple starting point. Whether you want to check how many records match your search, explore a subset of the data, or enrich your own files, you can do it all without writing a single line of code.

### Explore data with ease

* **Use prompts**. The fastest way to preview the data is through the [**AI Data Search**](https://dashboard.coresignal.com/data-assistant/) and [**API Playgrounds**](/self-service/features-and-tools/api-playgrounds.md#api-playgrounds-environment), where you can test queries and instantly see how many results match your criteria.
* **Enrich company data fast**. The [**Company Enricher**](https://dashboard.coresignal.com/enrich-data) tool allows you to upload a CSV containing company identifiers and instantly enrich it with relevant data fields, such as location, industry, and headcount, without technical setup.

## Dashboard

Let's start from the beginning – the dashboard's Home page. On the left, you’ll find quick access to the main features – tools and playgrounds. Your account – settings, details, and team management options are located in the upper-right corner. In the central workspace, you’ll see your API key, subscription plan details, recent queries, and – all in one place for easy navigation and control.

<figure><img src="/files/Ol5HAJVGVmwOw5YiR9K1" alt=""><figcaption></figcaption></figure>

## Credits

We use a credit-based system to manage API usage across both search and data collection. There are two types of credits:

* **Search credits** – used to send requests and preview data
* **Collect credits** – used to enrich full records

Both credit types are included in free trials and subscription plans. When you purchase a subscription, you only pay for Collect credits. Depending on the plan, you’ll receive double (or more) Search credits at no extra cost.

You can use credits to generate search queries and download data in **JSON**, **JSONL**, or **CSV** formats.

|                            | Search query cost | Collect query cost per record |
| -------------------------- | :---------------: | :---------------------------: |
| Base and Clean data        |         1         |               1               |
| Multi-source Company data  |         2         |               2               |
| Multi-source Employee data |         2         |               2               |
| Multi-source Jobs data     |         1         |               1               |

## Data download options

You can download data records by either calling our APIs directly or simply using our self-service platform.

{% hint style="info" %}
Please note that if you collect records one by one, you will get them in JSON format. For easier data processing, if you use the bulk download option, you can download data in JSONL or CSV format.
{% endhint %}

### Differences between JSON and JSONL files

**JSON (JavaScript Object Notation) files:** JSON files store data as a single, hierarchical structure, typically as an object or an array within curly or square brackets. Data in JSON files is one complete data structure.

**JSONL (JSON Lines) files:** JSONL files store data as individual JSON objects, each on a new line. Each line is a valid JSON object, but the file as a whole is not a valid JSON object because the objects are not enclosed within a larger array or object.

JSONL format is ideal for handling large datasets as it enables incremental processing of data. This is especially valuable for machine learning applications or for processing large datasets.


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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.coresignal.com/self-service/quickstart-tutorial.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.