# Embeddings The **Generate Embeddings** feature allows users to create embeddings for their dataset based on prompts. . The **Analysis** tab is located within the following path:\ \&#xNAN;**\[Project > Dataset > Evaluation > Embeddings]**

### How to Generate Embeddings #### 1. Initiating the Embedding Generation * To start generating embeddings, click on the **"Generate Embeddings"** button. * **Note**: Embeddings are generated on the basis of Prompt Column. #### 2. Applying Filters (Optional) * If you only want to generate embeddings for specific data points, you can apply filters. These filters help you narrow down the data points to focus on specific criteria before generating embeddings. * For example, you may choose to apply filters based on metadata or response columns. #### 3. Generating Embeddings * Once you’ve set your filters (if any), click **"Generate Embeddings"** to create a job.

#### 4. Viewing the Embeddings * When the job is complete, you’ll be able to visualise the embeddings directly in the interface. * **Note**: If the number of generated embeddings exceeds the visualization limit, the platform will automatically sample points to fit within the limit. #### 5. Color Coding for Insights * The generated embeddings can be color-coded based on a specific metric, user can toggle using the dropdown. * The color coding will differentiate between **pass** and **fail** data points, providing a clear visual cue for performance trends within the dataset.

datapoints color coded on the basis of metric

*** ### Important Notes * If you don’t apply any filters, embeddings will be generated for all available data points. * The platform automatically handles large datasets by sampling data points for visualization purposes if needed. --- # 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-catalyst/concepts/embeddings.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.