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Augment Agents and Prompts with Relevant Business Knowledge

Learning Objectives

After completing this unit, you’ll be able to:

  • Explain why retrieval augmented generation (RAG) improves the accuracy and relevance of LLM responses in agents and prompt templates.
  • Describe how to set up and use RAG in your Salesforce org.

What Is Retrieval Augmented Generation?

Retrieval augmented generation (RAG) is a popular way to ground prompt requests to large language models (LLMs). Grounding adds domain-specific knowledge or customer information to the prompt, giving the LLM context to respond more accurately to a question or task.

[alt text: RAG runtime flow: LLM prompt augmented with relevant information to instruct LLM response generation.]

To break it down, RAG:

  1. Retrieves relevant information from a knowledge store containing structured and unstructured content.
  2. Augments the prompt by combining this information with the original prompt.
  3. With the augmented prompt, the LLM generates a response.

Many LLMs are trained generally across the Internet on static and publicly available content. RAG adds domain-specific information to help LLMs give you better responses to your prompts. With RAG, you can extract valuable information from all sorts of content, such as service replies, cases, knowledge articles, conversation transcripts, RFP (request for proposal) responses, emails, meeting notes, frequently asked questions (FAQs), and more.

Quick-Start Agentforce Solutions With Agent Builder and Agentforce Data Library

Agent Builder allows you to seamlessly choose knowledge articles or upload files for retrieval by agents with just a few clicks. You can do this by selecting or creating an Agentforce Data Library, which is a library of content the agent uses to answer questions. Select the source from which the data library pulls relevant information: Salesforce Knowledge base or from files that you upload (text, HTML, and PDFs). At run time, your agent uses this information to ground LLM prompts and produce better, more accurate, and relevant LLM responses.

When you add a data library, you automatically create all the elements needed for a working, RAG-powered solution. If you want, you can then customize these elements to fine-tune RAG solutions for your use cases. We’ll get to that later.

Get Relevant Business Knowledge in Agents

Agents get relevant knowledge from a data library using the Answer Questions with Knowledge standard action. This action dynamically retrieves from the knowledge or file content you specified when you created or selected a library.

RAG detailed runtime flow for agents: Answer Questions with Knowledge action, query request and response, augmented prompt, and LLM response forward to the agent.

Each time the Answer Questions with Knowledge action is run:

  1. The action runs the associated prompt template. The retriever is invoked with a dynamic query.
  2. The query searches the data library.
  3. The query retrieves the relevant content.
  4. The original prompt is populated with information retrieved from the data library, and then submitted to the LLM.
  5. The LLM-generated response is forwarded to the agent.

Get Relevant Business Knowledge in Prompts

At run time, prompt templates pull relevant information from your data library to ground LLM prompts that result in more accurate LLM responses. If you’re using a custom prompt template, in Prompt Builder, simply embed an Einstein Search retriever that you select from the Resource field. You can also use a custom retriever that fine-tunes search settings for any given prompt.

RAG detailed runtime flow for prompts: query, vectorize, retrieve relevant content, augment, and submit to the LLM.

Each time a prompt template with a retriever is run:

  1. The retriever is invoked with a dynamic query that’s initiated from the prompt template.
  2. The query is vectorized (converted to numeric representations). Vectorization enables search to find semantic matches in the search index (which is already vectorized).
  3. The query retrieves the relevant content from the indexed data in the search index.
  4. The original prompt is populated with the information retrieved from the search index.
  5. The prompt is submitted to the LLM, which generates and returns the prompt response.

Advanced Customization in Data Cloud

When you add a data library, either in Agent Builder or from Setup, Salesforce automatically builds a RAG-powered solution using default settings for all of the components: vector data store, search index, retriever, prompt template, and standard action. You can set up and customize these components individually.

Data preparation steps: ingest, chunk, vectorize, and index.

Data preparation involves these tasks in Data Cloud.

  1. Connect (ingest) your unstructured data.
  2. Create a search index configuration that chunks and vectorizes the content. Data Cloud uses a search index to manage structured and unstructured content in a search-optimized way. You have two search options: vector search and hybrid search. Hybrid search combines vector + keyword search.
    • Chunking breaks the text into smaller units, reflecting passages of the original content, such as sentences or paragraphs.
    • Vectorization converts chunks into numeric representations of the text that capture semantic similarities.
  3. Store and manage the search index.

When a search index is created, Data Cloud automatically creates a default retriever for it. This retriever is a resource that you embed in a prompt template to search for, and return relevant information from the knowledge store. To support a variety of use cases, you can create custom retrievers in Einstein Studio that focus your search on the relevant subset of information to add to the prompt.

See RAG in Action

This video shows how easy it is to augment a prompt template using RAG.

Conclusion

Agentforce Data Library and RAG in Data Cloud are integrated with the Einstein generative AI platform. Natively incorporate RAG functionality into out-of-the-box apps like Agent Builder and Prompt Builder. With RAG, you can safely ground and improve your Agentforce solutions with proprietary data from a harmonized data model.

Resources

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