# Copy of Quickstart 1. Signing Up and Installation 2. Explore Preloaded Projects and Datasets 3. Explore Test Results 4. Execute your own Test! *** ## 1. Sign Up and Installation **Step 1: Create Your RagaAI Account** * Create you account on RagaAI Prism's public sandbox [here](https://platform.raga.ai/). * Provide your details and sign up for an account. * Confirm your email address through the verification link sent to your inbox. **Step 2: Accessing RagaAI** * Once your account is active, log in to the Prism dashboard. * Access the guided tour to familiarise yourself with the user interface and available features. *** ## 2. Explore Preloaded Projects and Datasets * **Access Projects**: On your dashboard, you'll see a list of projects. Each represents a unique AI application / use-case. Select any such project to perform a deep dive.

* **Explore a Run**: Every Run represents the execution of the test suite on a set of datasets and model. Select a Run to view all tests performed.

Runs - Every run of the RagaAI Test Suite on a group of datasets and models

*** ## 3. Explore Test Results **Access Test Results**: On the summary page, click view iterations and view results to explore test results for a specific dataset and model.

Summary of all tests executed in a test run

**Note**: An in-depth companion piece to every project and every test run is available on the [sandbox guide. ](/ragaai-prism/sandbox-guide.md) *** ## 4. \[Advanced] Execute your own Test! #### Step 1: Start the Test Run * Navigate to the RagaAI dashboard and open your project. * Click on the **'Initiate Run'** button located towards the right side of the dashboard.

#### Step 2: Access Keys Popup * A popup will appear displaying the **Access Key** and **Secret Key**. * These keys are crucial as they link the Google Colab file to your RagaAI account and the specific project you are working on.

RagaAI - Get you Access Key and Sceret Access Key

#### Step 3: Launch the Colab Notebook * Click on the **'Try It'** button within the popup. * This action will open a new tab with a Google Colab notebook preloaded with the necessary code cells for executing the test.

#### Step 4: Enter Your Access Keys * In the Colab notebook, locate the cell titled **'Put your ACCESS KEY & SECRET KEY'**. * Replace the placeholder text with your actual Access Key and Secret Key provided in the popup. #### Step 5: Execute the Colab Cells * The first cell typically contains the command to install the RagaAI testing platform library using `pip`. Run this cell to install the required library in your Colab environment. * After setting your Access Key and Secret Key, proceed to the cell labeled **'Import All raga lib from raga module'** and run it to import the necessary modules. * Set up a Run: This cell contains the code to initialise a test session with parameters like project name, run name, and your keys. Running this cell will start the test run using the onboarded dataset. * Run Tests: These cells contains the code to execute the different available tests. Alter the parameters like threshold, metric, etc and execute the cell #### Visualising Results Once the test is complete, you can easily visualise the results. Navigate back to our platform's user interface (UI). Inside the Runs tab, you'll find a new run containing results for the tests executed by you. *** ## **Next Steps** An in-depth companion piece to every project and every test run is available on the [sandbox guide](/ragaai-prism/sandbox-guide.md). We hope this Quickstart Guide helps you take the first step towards efficient AI model testing with the RagaAI sandbox. [ ](/ragaai-prism/sandbox-guide.md) --- # 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.raga.ai/ragaai-prism/copy-of-quickstart.md?ask= ``` 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.