> ## Documentation Index
> Fetch the complete documentation index at: https://docs.poly.ai/llms.txt
> Use this file to discover all available pages before exploring further.

# RAG

> How retrieval-augmented generation powers topic matching.

This page explains how your agent finds the right topic to answer a caller's question. You do not need to configure RAG directly – it works automatically based on how you structure your [Managed Topics](/managed-topics/introduction) and [Connected Knowledge](/connected-knowledge/introduction).

## What is RAG?

Retrieval-Augmented Generation (RAG) is a technique where the system first searches a knowledge source for relevant content, then feeds that content to a language model to generate an accurate response.

## Components of RAG

1. **Retrieval component**: Searches the knowledge for relevant information based on the input query.
2. **Augmentation**: Uses retrieved information to enhance the original query with additional context.
3. **Generation component**: Generates responses using a language model, integrating both the query and retrieved information.

## How RAG works in Agent Studio

PolyAI uses RAG to match user queries to Knowledge topics and generate contextual responses. Here is how it works in Agent Studio:

```mermaid theme={"theme":{"light":"github-light","dark":"github-dark"}}
flowchart LR
    A[User Query] --> B[Retriever]
    B --> C[Knowledge Topics]
    C --> D[Top Matches]
    D --> E[LLM]
    E --> F[Response]
    E --> G[Action]
```

1. **Query processing**: When a caller provides a query, the RAG framework is initiated.
2. **Retrieval**: The retriever component searches the structured knowledge to find matching topics. The knowledge is organized to optimize retrieval performance and ensure precise matches.
3. **Generation**: The LLM uses the retrieved information to select and generate the response.

<Tip>
  Write clear, specific topic names and realistic sample questions for best results. These are key signals the retriever uses to find the right match. You can add up to **20** sample questions per topic – more questions help the retriever find the right match. For best retrieval and generation quality, use PolyAI's [Raven](/agent-settings/raven) model – it is robust to irrelevant retrieved content and will say "I don't know" rather than hallucinate when information isn't available.
</Tip>

## Managed Topic structure for RAG

Each Managed Topic is structured for effective retrieval. A topic includes:

* **Topic name**: The FAQ name or category of the information.
* **Sample Questions**: Example queries that callers might use. These help RAG understand user intent and improve matching accuracy.
* **Content**: The information you want the agent to provide to users.
* **Action**: Specific actions triggered by the query, such as calling a function, initiating a workflow, or handing off to a human agent.

## Disabling topics at runtime

For deterministic control over which topics are available during a conversation, you can disable specific Managed Topics from a Python function using `conv.disable_kb_topics()`. Disabled topics are excluded from retrieval until the conversation ends or you re-enable them. See [Disable KB topics](/tools/classes/disable-kb-topics).

## Why RAG?

You do not need to retrain a model when you update your Knowledge. RAG retrieves from the current Knowledge at query time, so updates are available as soon as they are [promoted to the target environment](/environments-and-versions/introduction).

<Note>
  Behavior may vary depending on your agent's configuration. For example, agents using the real-time (speech-to-speech) model may trigger retrieval differently than standard voice agents. For multilingual agents, see [multilingual configuration](/agent-settings/multilingual) for guidance on setting up Knowledge topics across languages.
</Note>