--- schema_version: "1" id: 5efd370778f5a7db01d187a82f2b46c2047eeb93f3cad80902dde2c243ab5178 title: "Example: Agentic RAG with Advanced Data 360 Setup" collection: admin version: current locale: en-us portal_url: https://docs.salesforce.com/admin/current/en-us/ai/264-0-0/generative_ai_rag_example.md canonical_url: https://help.salesforce.com/s/articleView?id=ai.generative_ai_rag_example.htm&release=264.0.0&type=5 source_site: help.salesforce.com content_type: documentation content_hash: 97457884131ceb22529e6da24a48a62119f09a7ac43aa2478bb21b542ea32ee0 description: This example walks you through the end-to-end steps to implement an agent that's grounded with knowledge using Retrieval Augmented Generation (RAG) in Data 360. products: - Agentforce - Cross Cloud release: "264" --- # Example: Agentic RAG with Advanced Data 360 Setup This example walks you through the end-to-end steps to implement an agent that's grounded with knowledge using Retrieval Augmented Generation (RAG) in Data 360. ### Required Editions
Available in: Enterprise, Performance, and Unlimited Editions with an Einstein for Sales, Einstein for Platform, Einstein for Service, Einstein 1 Service, or Einstein GPT Service add-on. To purchase add-ons, contact your Salesforce account executive.
You don’t need any prior knowledge of Data 360, AI Models (formerly Einstein Studio), or Agentforce. Implement this scenario hands-on in a playground org by taking the [Advanced RAG with Data 360 and Agentforce](https://trailhead.salesforce.com/content/learn/modules/advanced-rag-with-data-360-and-agentforce) Trailhead badge. For a high-level overview, see [About Retrieval Augmented Generation](https://help.salesforce.com/s/articleView?id=ai.generative_ai_rag_about.htm&release=264.0.0&type=5 "Learn how Retrieval Augmented Generation (RAG) in Data 360 enhances your Agentforce and Einstein generative AI solutions with knowledge.") and [Retrieval Augmented Generation: Quick Look](https://trailhead.salesforce.com/content/learn/modules/retrieval-augmented-generation-quick-look) in Trailhead. ## Approaches to Building RAG-Powered Agents There are two main approaches to setting up a RAG-powered agent. - Quick Start. The benefit of this approach is that you’re up and running with a working solution for your agent without needing to configure all the individual components under the hood. The quick-start approach is to create a data library using Agentforce Data Library. When you create a data library, you select a data source (HTML, PDFs, Knowledge object, uploaded files, web search, or custom retrievers) to search, and assign it to an agent. Agentforce Data Library generates all the underlying RAG solution components automatically. To learn more, see [Agentforce Data Library](https://help.salesforce.com/s/articleView?id=ai.data_library_parent.htm&release=264.0.0&type=5 "Improve accuracy, add personalization, and build trust in gen AI responses when you assign data libraries to your Agentforce features. Agentforce Data Library uses grounding to index your knowledge articles and fields, your file uploads, or web sources, so that AI agents know which information to base responses on. With this index, AI agents can ensure the accuracy of LLM responses against your organization’s information so that you get the best results.") in Salesforce Help and [Agentforce Data Library Basics](https://trailhead.salesforce.com/content/learn/modules/agentforce-data-library-basics) in Trailhead. - Advanced. The benefit of this approach is that you work directly with the builder tools and gain exposure to the full range of settings and options you can use to customize a RAG solution for a given use case. This example walks through the advanced approach. By setting up each component individually, you gain an end-to-end, visceral understanding of its configuration options and how it fits into the RAG solution stack. You’re welcome to follow along with the instructions in a Salesforce Developer org. Note that your results may vary. ## Scenario in this Example RAG is expert at extracting relevant information from unstructured data, which includes documents, web sites, and free-form text fields in structured data. This scenario uses free-form text (subject and description) that you add to a Salesforce Case object. An agent can use this information to inform user questions about product issues. Other data source options are described elsewhere in Salesforce Help and Trailhead. ## Step 1: Set Up a Salesforce Developer Edition Org This step walks you through instructions to get and prepare a Salesforce Developer Edition org, which is required to complete the remaining steps in this example. To sign up, click [https://www.salesforce.com/form/developer-signup/?d=pb](https://www.salesforce.com/form/developer-signup/?d=pb). ![Note](https://sf-zdocs-cdn-prod.zoominsoftware.com/tdta-ai-generative_ai-264-0-0-production-enus/f8ef6498-d7b2-44e3-9b34-ffd5a850aae9/images/icon_note.png) Note - If you’d prefer to use a different Salesforce org, verify that it offers comparable functionality: Data 360 is enabled and provisioned, Einstein is enabled, Agentforce is enabled, Prompt Builder is available, and it’s OK for you to add cases. Note that your user permissions and other org settings can affect your ability to complete all the steps in this example. - If you're following along with the instructions and your org runs into unexpected issues, try refreshing your browser window. You can also try going back and repeating previous instructions. 1. Log into the Salesforce Developer Edition org. 2. Check your Data Cloud setup. From Setup, in the Quick Find box, enter Data Cloud Setup. If you see **Your Data Cloud instance is live** under Your Home Org Details, then Data Cloud is already enabled. **Data Cloud turned on and running** ![Screen area that shows when your Data Cloud instance is live.](https://sf-zdocs-cdn-prod.zoominsoftware.com/tdta-ai-generative_ai-264-0-0-production-enus/f8ef6498-d7b2-44e3-9b34-ffd5a850aae9/generative_ai/images/generative_ai_rag_example_dc_enabled.png) 3. If Data Cloud isn’t already enabled, to enable it, follow the instructions in-app or in [Turn On Data Cloud](https://help.salesforce.com/s/articleView?id=data.c360_a_setup_provision.htm&language=en_US) in Data Cloud Help). Click **Get Started** to initiate the setup process. **Get Started with Data Cloud Setup** ![Screen area that shows Get Started button and automated steps for initial Data Cloud setup.](https://sf-zdocs-cdn-prod.zoominsoftware.com/tdta-ai-generative_ai-264-0-0-production-enus/f8ef6498-d7b2-44e3-9b34-ffd5a850aae9/generative_ai/images/generative_ai_rag_example_dc_getstarted.png) ![Note](https://sf-zdocs-cdn-prod.zoominsoftware.com/tdta-ai-generative_ai-264-0-0-production-enus/f8ef6498-d7b2-44e3-9b34-ffd5a850aae9/images/icon_note.png) Note A Data Cloud initial setup can take around 30 minutes or more. 4. When finished, verify that Data Cloud is enabled. Confirm that **Your Data Cloud instance is live** appears under Your Home Org Details. 5. Check your Einstein Generative AI Setup. From Setup, in the Quick Find box, enter Einstein Setup. Make sure that **Einstein**, **Global Language Support for Prompt Builder**, and **Deploy Prompt Templates** are all turned on. **Turn On Einstein Options** ![Einstein Setup with Einstein, Global Language Support for Prompt Builder and Deploy Prompt Templates turned on.](https://sf-zdocs-cdn-prod.zoominsoftware.com/tdta-ai-generative_ai-264-0-0-production-enus/f8ef6498-d7b2-44e3-9b34-ffd5a850aae9/generative_ai/images/generative_ai_rag_example_einstein_setup.png) ![Note](https://sf-zdocs-cdn-prod.zoominsoftware.com/tdta-ai-generative_ai-264-0-0-production-enus/f8ef6498-d7b2-44e3-9b34-ffd5a850aae9/images/icon_note.png) Note When turned on, it can take a few minutes for Einstein enablement to take effect. 6. From Setup, in the Quick Find box, enter Agent, and then select **Agentforce Agents**. To enable Agentforce, turn on **Agentforce**. If you can't find **Agentforce Agents**, refresh your browser window or wait a little longer while Einstein enablement takes effect. **Turn On Agentforce** ![Screen area that shows when your Data Cloud instance is live.](https://sf-zdocs-cdn-prod.zoominsoftware.com/tdta-ai-generative_ai-264-0-0-production-enus/f8ef6498-d7b2-44e3-9b34-ffd5a850aae9/generative_ai/images/generative_ai_rag_example_agentforce_setup.png) ## Step 2: Add Case Data Create at least five Case records that name a product and describe a problem that a customer is having. 1. In App Launcher, enter Service and open Service Cloud. 2. Select **Cases -> New** and add your cases. For each case, specify a subject, a description that details a customer problem, and select a case origin. Skip other fields and accept the defaults. Here’s some example data to enter. | Example Subject | Example Description | | --- | --- | | Power cord caught fire | The power cord caught fire the first time I tried to use my toaster oven to cook a pizza. | | Toaster oven won’t start | The toaster oven is plugged in but when I push the power button, nothing happens. Nothing lights up. There’s no power. | | Toaster temperature not heating up enough | I turned the toaster oven to the broil setting but the temperature doesn’t go above lukewarm even after 10 minutes. | | Toast burns on lowest temperature setting | The toaster oven always burns my toast, even when I select the Light setting. | | Crumbs caught fire | Crumbs from my bagel dropped to the bottom of the toaster oven and caught fire. | | Toaster turns off suddenly for no reason | If I’m cooking anything for more than 15 minutes, the toaster oven shuts off automatically. | | Toaster oven arrived broken | The glass window was broken when I opened the package. | | Package arrived crushed | The toaster oven was visibly damaged when I opened the crushed delivery package. | ## Step 3: Ingest Case Data into Data Cloud​ RAG-powered solutions use search indexes that are created in Data Cloud. To ingest your free-form case data into Data Cloud, create a data stream. 1. In App Launcher, enter and select **Data Cloud**. 2. Select the Data Streams tab, click **New**, select **Salesforce CRM**, and click **Next**. 3. Click **View Objects**, search for and select **Case**, and click **Next**. **Select the Case Object** ![Case object is selected for ingestion.](https://sf-zdocs-cdn-prod.zoominsoftware.com/tdta-ai-generative_ai-264-0-0-production-enus/f8ef6498-d7b2-44e3-9b34-ffd5a850aae9/generative_ai/images/generative_ai_rag_example_ingest.png) 4. For the Object Category, select **Profile**. 5. All fields are selected by default. In the **Standard Fields** tab, select only these fields (deselect the others). | Field | Description | | --- | --- | | System Modstamp | Required | | Subject | A summary of the customer’s issue that can be used to answer the customer’s question. | | Description | Additional details that can be used to answer the customer’s question. | | Case ID | Required but not needed for this simple use case. For more advanced use cases, you can use the Case ID to get additional related information, such as from an Account. | ![Note](https://sf-zdocs-cdn-prod.zoominsoftware.com/tdta-ai-generative_ai-264-0-0-production-enus/f8ef6498-d7b2-44e3-9b34-ffd5a850aae9/images/icon_note.png) Note The Case Comment field can also provide useful data for RAG searches, but it’s not needed for this simple example. 6. Accept the selections in the **Custom Fields** tab. Then click **Next**. 7. Click **Deploy**. ![Note](https://sf-zdocs-cdn-prod.zoominsoftware.com/tdta-ai-generative_ai-264-0-0-production-enus/f8ef6498-d7b2-44e3-9b34-ffd5a850aae9/images/icon_note.png) Note Ingesting data can take time. 8. To check status, in the Data Streams menu, click the down arrow next to your new data stream, and select **Update Status**. When **Last Run Status** is **Success** and **Total Records** has a number, your data is in Data Cloud. **Data Ingested into Data Cloud** ![Case data was ingested into Data Cloud.](https://sf-zdocs-cdn-prod.zoominsoftware.com/tdta-ai-generative_ai-264-0-0-production-enus/f8ef6498-d7b2-44e3-9b34-ffd5a850aae9/generative_ai/images/generative_ai_rag_example_ingested.png) ## Step 4: Inspect Your Data Lake Object (DLO)​ The data stream creates a Data Lake Object (DLO) called `Case_Home__dll`. It’s useful to inspect the ingested data, which can help you debug and optimize your RAG solution. 1. Go to the Data Explorer tab in Data Cloud, select **Data Lake Object** as the Object Type, and select **Case\_Home**. Don’t change the data space. 2. Click **Edit Columns**. Move **Potential Liability** to **Available Columns** if it’s not already there. Move **Subject** to **Selected Columns** if it’s not already there. Leave all other columns where they are, and click **Done**. **Edit Columns for Case\_Home DLO** ![Select columns for Case\_Home Data Lake Object (DLO).](https://sf-zdocs-cdn-prod.zoominsoftware.com/tdta-ai-generative_ai-264-0-0-production-enus/f8ef6498-d7b2-44e3-9b34-ffd5a850aae9/generative_ai/images/generative_ai_rag_example_dlo_columns.png) 3. Click the **Description** column twice to sort and show your case data at the top. **Case Data in Case\_Home DLO** ![Case Data in Case\_Home Data Lake Object (DLO).](https://sf-zdocs-cdn-prod.zoominsoftware.com/tdta-ai-generative_ai-264-0-0-production-enus/f8ef6498-d7b2-44e3-9b34-ffd5a850aae9/generative_ai/images/generative_ai_rag_example_dlo_data.png) ![Tip](https://sf-zdocs-cdn-prod.zoominsoftware.com/tdta-ai-generative_ai-264-0-0-production-enus/f8ef6498-d7b2-44e3-9b34-ffd5a850aae9/images/icon_tip.png) Tip Developers: To query your Case DLO, click **Copy SOQL** and copy the query to use in Developer Console or in the [Data Cloud Query Editor](https://help.salesforce.com/s/articleView?id=data.c360_a_add_queries_to_a_query_workspace.htm&type=5&language=en_US). You can also query the DLO using a REST client like Postman. ## Step 5: Map Your Case DLO to a Case DMO​ Map a DLO to a Data Model Object (DMO) to add an additional layer of structure to your data and make it easier to query and interact with. Map the **Case\_Home** DLO to the **Case** DMO. ![Note](https://sf-zdocs-cdn-prod.zoominsoftware.com/tdta-ai-generative_ai-264-0-0-production-enus/f8ef6498-d7b2-44e3-9b34-ffd5a850aae9/images/icon_note.png) Note DLOs and DMOs are powerful tools for building RAG solutions. For example, you can perform data cleansing on your DLO, map multiple data sources to a single DMO, or perform identity resolution. 1. In the Data Lake Objects tab, open **Case\_Home**. 2. In the Data Mapping panel, click **Start**. 3. Under Data Model Entities, click **Select Objects**, search for **Case** in the **All Objects** panel, click the + sign next to it, and click **Done**. DLO and DMO fields are automatically mapped. **DLO-to-DMO Mapping** ![DLO mapped to DMO.](https://sf-zdocs-cdn-prod.zoominsoftware.com/tdta-ai-generative_ai-264-0-0-production-enus/f8ef6498-d7b2-44e3-9b34-ffd5a850aae9/generative_ai/images/generative_ai_rag_example_dlo_map.png) 4. Save your work. ## Step 6: Inspect Your Data Model Object (DMO)​ The mapping performed in the previous step created a DMO called `ssot__Case__dlm`. 1. In Data Explorer, for **Object**, select **Data Model Object**, then search for and select **Case**. 2. Click the **Description** column twice to sort and show your case data at the top. **Data in the DMO** ![Data in the DMO.](https://sf-zdocs-cdn-prod.zoominsoftware.com/tdta-ai-generative_ai-264-0-0-production-enus/f8ef6498-d7b2-44e3-9b34-ffd5a850aae9/generative_ai/images/generative_ai_rag_example_dmo_data.png) ![Tip](https://sf-zdocs-cdn-prod.zoominsoftware.com/tdta-ai-generative_ai-264-0-0-production-enus/f8ef6498-d7b2-44e3-9b34-ffd5a850aae9/images/icon_tip.png) Tip Developers: To query your Case DMO, click **Copy SOQL** and copy the query to use in Developer Console or the [Data Cloud Query Editor](https://help.salesforce.com/s/articleView?id=data.c360_a_add_queries_to_a_query_workspace.htm&type=5&language=en_US). You can also query the DLO using a REST client like Postman. ## Step 7: Build Your Search Index​ A search index makes your text searchable so that your RAG solution can find and retrieve chunks of text that are relevant to the user’s input. In this example, you create a hybrid search index to find relevant content based on both semantic similarity and keywords. 1. In Data Cloud, select the Search Index tab, and click **New**. 2. Select **Advanced Setup** and click **Next**. 3. Select **Hybrid Search** as the Search Type and **Case** as the Source Object, then click **Next**. 4. Click **Manage Fields** and select **Description** and **Subject**, then click **Save**. 5. Click **Next** repeatedly to accept the remaining default values, and then click **Save** to create your search index. 6. Click the Search Index tab to refresh it. Your Case search index is ready when the **Search Index Last Run Status** is **Ready** (about 10 minutes). 7. Click the Data Model tab to see the created search index files: **Search Index Files** ![Search index files with Case\_chunk\_dlm and Case\_index\_dlm highlighted.](https://sf-zdocs-cdn-prod.zoominsoftware.com/tdta-ai-generative_ai-264-0-0-production-enus/f8ef6498-d7b2-44e3-9b34-ffd5a850aae9/generative_ai/images/generative_ai_rag_example_search_index.png) - text chunks (`Case_chunk_dlm`) - a hybrid index (`Case_index_dlm`) It’s helpful to know how to inspect text chunks for debugging purposes or feature optimization. For example, chunks might reveal why an LLM is responding in unexpected ways. In Prompt Builder, you’re able to view the retrieved chunks, as you’ll see later in this example. ![Tip](https://sf-zdocs-cdn-prod.zoominsoftware.com/tdta-ai-generative_ai-264-0-0-production-enus/f8ef6498-d7b2-44e3-9b34-ffd5a850aae9/images/icon_tip.png) Tip Developers: - To inspect the data chunks in the search index you created, or to ask natural language query questions, you can use the query API in Apex or the queryDataV2 endpoint in the Data Cloud Connect REST API. - To see the configurations for all search indexes in an org, use /ssot/search-index. - Searching with APIs helps you build a proof of concept solution and debug. You can also try different types of search, such as prefiltering or using a vector search, without building a retriever each time. ## Step 8: Build a Retriever A retriever is a wrapper for Einstein Search operations. You create a custom retriever in AI Models to refine the search so that the retrieved content is more relevant to your use case. ![Note](https://sf-zdocs-cdn-prod.zoominsoftware.com/tdta-ai-generative_ai-264-0-0-production-enus/f8ef6498-d7b2-44e3-9b34-ffd5a850aae9/images/icon_note.png) Note An individual retriever can fetch data from only one search index. A search index can index only one DMO. If your feature needs to ground on data from multiple data sources, use an ensemble retriever. 1. In Data Cloud, select AI Models , select **Retrievers**, and click **New Retriever**. 2. Select **Individual Retriever** and click **Next**. 3. Select **Data Cloud** and accept the default data space. 4. Select the DMO (**Case**) and search index configuration (**Case Hybrid Search**), then click **Next**. 5. For filters, accept the default (All Documents) and click **Next**. 6. For **Fields to Return**, add these three fields: 1. Case object’s **Subject** field (**DirectAttribute > Case > Subject**) 2. Case object’s **Description** field (**DirectAttribute > Case > Description)** 3. Chunk field (**RelatedAttributes > Case chunk > Chunk**) ![Tip](https://sf-zdocs-cdn-prod.zoominsoftware.com/tdta-ai-generative_ai-264-0-0-production-enus/f8ef6498-d7b2-44e3-9b34-ffd5a850aae9/images/icon_tip.png) Tip Select the field name first. The associated field label is selected automatically. **Fields to Return in the Retriever** ![Search index files with Case\_chunk\_dlm and Case\_index\_dlm highlighted.](https://sf-zdocs-cdn-prod.zoominsoftware.com/tdta-ai-generative_ai-264-0-0-production-enus/f8ef6498-d7b2-44e3-9b34-ffd5a850aae9/generative_ai/images/generative_ai_rag_example_retriever_fields.png) 7. Click **Next**, then click **Save**. Name this retriever and create it. 8. On the Retrieval Datails page, copy the **API Name**, which you'll use in the next step. 9. Click **Activate**. ## Step 9 - Create a Prompt Template and Add Your Retriever​ A prompt template defines the structure of the prompt that is sent to the LLM. You can add text instructions and resources, such as retrievers, Flows, customer input, or other resources. A prompt template inherits characteristics from a prompt template type, which contains hidden pre- and post-processing instructions that affect how the resolved prompt is used at runtime. In this example, you create a new prompt template based on the Answer Questions with Knowledge prompt template. 1. From Setup, in the Quick Find box, enter Prompt Builder, and then select **Prompt Builder**. 2. Click **New Prompt Template**. 3. For Prompt Template Type, select **Knowledge Answers**. 4. Name your prompt template (for example, Answer Questions With Case Data) and click **Next**. 5. Enter instructions into the prompt. Copy and paste from the following example, which comes from the Answer Questions With Knowledge prompt template. ``` ### INSTRUCTIONS 1. Analyze the query: Carefully read and understand the user's question or issue from the QUESTION section. 2. Search CASE: Review the provided company CASE to find relevant information. 3. Evaluate information: Determine if the available information in the CASE section is sufficient to answer the QUESTION. 4. Formulate response: To generate a reply to the user, you must follow these rules a. Find the article-chunk(s) that is most relevant to answer the user query and VERBATIM extract the ID of the article to set field in the response JSON. If you are unable to find the relevant article, set to NONE. b. Use the relevant article-chunk to generate the response that exactly answers the user's question and set the field. c. If the user request cannot be answered by the case information provided, set the to NONE and to "Sorry, I can't find an answer based on the available articles." 5. Refine and deliver: Ensure your response is polite, professional, and concise. 6. Review response: Make sure that have followed all of the above instructions, respond in the desired output format and you only strictly stick to the provided CASE only to formulate your answer. Sample_Case_Retriever_1Cx_cS3a95815db ### CASE: [Insert Retriever Here] ### QUESTION: {!$Input:Query} ### Response: ``` 6. Delete `[Insert Retriever Here]` and leave your cursor at that spot. 7. Click **Insert Resource**, select **Einstein Search > Case**, and add your retriever. 8. In the retriever settings panel: 1. For **Search Text**, select **Free Text**, then **Query**. 2. For **Output Fields**, select **Chunk**. 3. For this example, set **Number of Results** to 5. 9. In the prompt template, delete the `InputQuery` under QUESTION and leave your cursor in that spot. 10. In the settings panel on the left, under **Search Parameters**, click **Search Text**, select **Free Text**, **Query**, and copy the query input (`Input:Query`). Under QUESTION:, paste `Input:Query`. **Input:Query** ![Input:Query in prompt template instructions.](https://sf-zdocs-cdn-prod.zoominsoftware.com/tdta-ai-generative_ai-264-0-0-production-enus/f8ef6498-d7b2-44e3-9b34-ffd5a850aae9/generative_ai/images/generative_ai_rag_example_prompt_template.png) 11. Save your work and then click **Activate**. ## Step 10: Test Your Prompt Template​ Test the prompt template you just created. 1. Click **Preview**. 2. In Preview Settings, expand **Inputs** if needed. 3. Enter a question into the Query, such as What are some common issues with the toaster oven product? 4. For Retriever ID, add your retriever ID. (In AI Models, select Retrievers, select your retriever and, on the retriever details page, copy your retrievers’s API name, then paste it in the Retriever ID field). 5. Click **Preview** to see the LLM’s response. **Preview Shows the Prompt Request and the LLM Response** ![Preview Shows the Prompt Request and the LLM Response.](https://sf-zdocs-cdn-prod.zoominsoftware.com/tdta-ai-generative_ai-264-0-0-production-enus/f8ef6498-d7b2-44e3-9b34-ffd5a850aae9/generative_ai/images/generative_ai_rag_example_preview.png) - **Resolved Prompt** shows the resolved prompt that was sent to the LLM. The resolved prompt contains the chunks of data that your retriever fetched. That’s RAG in action! - **Generated Response** shows the prompt template’s output that you can surface to customers. ![Note](https://sf-zdocs-cdn-prod.zoominsoftware.com/tdta-ai-generative_ai-264-0-0-production-enus/f8ef6498-d7b2-44e3-9b34-ffd5a850aae9/images/icon_note.png) Note If you get an error, try again with the same question or a slightly different one. You can also try refreshing your browser window. ## Step 11: Build an Agent that Answers Questions Using Case Knowledge Finally, create an agent that uses your prompt template to answer questions using your case data. ![Tip](https://sf-zdocs-cdn-prod.zoominsoftware.com/tdta-ai-generative_ai-264-0-0-production-enus/f8ef6498-d7b2-44e3-9b34-ffd5a850aae9/images/icon_tip.png) Tip Developers: You don’t need to consume your prompt template from an action. Developers can consume it from Apex or a Flow instead. 1. Create an Agent Action​ 1. From Setup, in the Quick Find box, enter Agentforce Assets, and then select **Agentforce Assets**. 2. Click the Actions tab. 3. Click **New Agent Action**. 4. For the **Reference Action Type**, select **Prompt Template**. 5. For the **Reference Action**, select your prompt template. If you don’t see your prompt template, make sure it’s active. 6. (Optional) Name your action something like Answer Questions With Case Data, then click **Next**. 7. Add **Agent Action Instructions**. For example: `Answer questions that customers have about product issues. Use the provided customer cases, where the description and subject tell you what is going wrong, or how the customer is struggling. For example: “What are some common issues with the toaster oven?”` 8. For Inputs, fill in the required fields. Examples: **Loading Text**: Getting an answer… **Inputs instructions:** Use the retriever that is hard coded in the prompt template. **Query Instructions** Required. A string created by generative AI to be used in the Case search. **Collect data from user**: (make sure it's selected under **Query Instructions**) **Citation Mode Instructions**: Do not generate citations. Accept all other defaults. 9. For Outputs, for the Prompt Response, enable **Show in Conversation**. 10. Accept all other defaults and click **Finish**. 11. Select the created agent action to view its details. **Agent Action Details** ![Details of an agent action.](https://sf-zdocs-cdn-prod.zoominsoftware.com/tdta-ai-generative_ai-264-0-0-production-enus/f8ef6498-d7b2-44e3-9b34-ffd5a850aae9/generative_ai/images/generative_ai_rag_example_agent_action.png) 2. Create an Agent 1. From Setup, in the Quick Find box, enter Agentforce Agents, and then select **Agentforce Agents**. 2. Click **New Agent**, select **Agentforce Service Agent**, and click **Next**. 3. Deselect everything except **General FAQ**, then click **Next**. 4. For company, add A company that sells toaster ovens, accept the other defaults, and click **Next**. 5. Skip the data library and click **Create**. ![Note](https://sf-zdocs-cdn-prod.zoominsoftware.com/tdta-ai-generative_ai-264-0-0-production-enus/f8ef6498-d7b2-44e3-9b34-ffd5a850aae9/images/icon_note.png) Note If you see an error, wait a minute or two and check whether the agent was created. If not, try refreshing the browser window or repeating the instructions in this step. 3. Add a New Subagent to Your Agent​ Create a new subagent to consume the action from the previous step. 1. In your agent, select **New -> + New Subagent**. 2. For what you want this subagent to do, enter Answer questions with Case information, and click **Next**. 3. For **Classification Description**, replace the default value with something like : This subagent addresses user inquiries related to toaster oven cases and provides relevant details. 4. For **Scope**, accept the default instructions. 5. For **Instructions**, replace the first instruction with: Gather as much information as possible from the user by answering clarifying questions. 6. Delete all the other default instructions and click **Next**. 7. Select the action you created earlier (**Answer Questions with Case Data**), then click **Finish**. 8. Click **Activate** to activate your agent. 9. If you see the **Agent Readiness Checklist**, click **Ignore & Activate**. The best practice is to review and resolve any issues. However, that's not needed for our example. 10. Test your agent. In the Conversation Preview window, ask a question such as What are some common problems with toaster ovens? **Test Your Agent** ![Test your agent using the Converstation Preview window.](https://sf-zdocs-cdn-prod.zoominsoftware.com/tdta-ai-generative_ai-264-0-0-production-enus/f8ef6498-d7b2-44e3-9b34-ffd5a850aae9/generative_ai/images/generative_ai_rag_example_test.png) Note that the answer is not a generic answer about all toaster ovens. Instead, it reflects the information you entered as case subjects and descriptions. It’s easy to see how the LLM response is grounded in the very data source you populated. ## Possible Next Steps This example walked you through the advanced steps for setting up RAG in Data 360. From here, you can continue to explore how RAG can help you implement different use cases in your org. Consider these possibilities. - Use the Salesforce Developer Edition org to experiment with different settings and approaches. For example, build hybrid and vector indexes and compare the results, or use experiment with different chunking strategies. To learn about all the available settings: | Task | Learning Resources | | --- | --- | | Ingest Data into Data 360 | [_Salesforce Help (Data 360)_: Search for AI, Automation, and Analytics](https://help.salesforce.com/s/articleView?id=data.c360_a_search_index_ground_ai.htm&type=5&language=en_US) | | Build a Search Index | [_Salesforce Help (Data 360)_: Manage Search Indexes](https://help.salesforce.com/s/articleView?id=data.c360_a_search_index_manage.htm&language=en_US) | | Create a custom retriever | [_Salesforce Help (Data 360)_: Retrieve Data](https://help.salesforce.com/s/articleView?id=data.c360_a_ai_retriever.htm&type=5&language=en_US) | | Create a prompt template that uses the retriever | [Ground with Knowledge Using Retrieval Augmented Generation](https://help.salesforce.com/s/articleView?id=ai.prompt_builder_ground_rag.htm&release=264.0.0&type=5 "Retrieval augmented generation (RAG) in Data 360 improves the quality, accuracy, and relevance of your LLM-generated responses. Enhance your prompt templates using retrievers with Einstein Search. Add relevant knowledge from unstructured text sources into your LLM prompts. Retrieve knowledge from articles, emails, chat transcripts, and other sources.") | | Create an agent | [Ground Agentforce in Data](https://help.salesforce.com/s/articleView?id=ai.agent_parent_data.htm&release=264.0.0&type=5 "A great agent uses your data to provide high-quality responses. Learn about methods for connecting agents to knowledge sources, including internal data, unstructured documents, and web search.") | - Expand the use case. For example, try ingesting the Case's status field, then filtering for resolved cases only. Or add case comments to the case records, then add that field in subsequent tasks so that the agent can use it to answer questions. Or ingest in case-related data from different sources and experiment with - Try different use cases that, in your orgnization, are candidate projects for RAG implementations. - Learn more about RAG. Consider these Trailhead modules: - [Retrieval Augmented Generation: Quick Look](https://trailhead.salesforce.com/content/learn/modules/retrieval-augmented-generation-quick-look) - [Unstructured Data in Data 360](https://trailhead.salesforce.com/content/learn/projects/unstructured-data-in-data-cloud) - [Search Index Types in Data 360: Quick Look](https://trailhead.salesforce.com/content/learn/modules/search-index-types-data-cloud-quick-look) - [Hybrid Search for RAG: Quick Look](https://trailhead.salesforce.com/content/learn/modules/hybrid-search-for-rag-quick-look) - [Knowledge Grounding for AI: Quick Look](https://trailhead.salesforce.com/content/learn/modules/knowledge-grounding-for-ai-quick-look) - [Agentforce Data Library Basics](https://trailhead.salesforce.com/content/learn/modules/agentforce-data-library-basics)