> ## Documentation Index
> Fetch the complete documentation index at: https://meilisearch-6b28dec2-mintlify-code-samples.mintlify.site/llms.txt
> Use this file to discover all available pages before exploring further.

# What is conversational search?

> Conversational search allows people to make search queries using natural languages.

Conversational search is an AI-powered search feature that allows users to ask questions in everyday language and receive answers based on the information in Meilisearch's indexes.

## When to use conversational vs traditional search

Use conversational search when:

* Users need easy-to-read answers to specific questions
* You are handling informational-dense content, such as knowledge bases
* Natural language interaction improves user experience

Use traditional search when:

* Users need to browse multiple options, such as an ecommerce website
* Approximate answers are not acceptable
* Your users need very quick responses

<Warning>
  Conversational search is still in early development. Conversational agents may occasionally hallucinate inaccurate and misleading information, so it is important to closely monitor it in production environments.
</Warning>

## Conversational search user workflow

### Traditional search workflow

1. User enters keywords
2. Meilisearch returns matching documents
3. User reviews results to find answers

### Conversational search workflow

1. User asks a question in natural language
2. Meilisearch retrieves relevant documents
3. AI generates a direct answer based on those documents

## Implementation strategies

### Retrieval Augmented Generation (RAG)

In the majority of cases, you should use the [`/chats` route](/reference/api/chats) to build a Retrieval Augmented Generation (RAG) pipeline. RAGs excel when working with unstructured data and emphasise high-quality responses.

Meilisearch's chat completions API consolidates RAG creation into a single process:

1. **Query understanding**: automatically transforms questions into search parameters
2. **Hybrid retrieval**: combines keyword and semantic search for better relevancy
3. **Answer generation**: uses your chosen LLM to generate responses
4. **Context management**: maintains conversation history by constantly pushing the full conversation to the dedicated tool

Follow the [chat completions tutorial](/learn/chat/getting_started_with_chat) for information on how to implement a RAG with Meilisearch.

### Model Context Protocol (MCP)

An alternative method is using a Model Context Protocol (MCP) server. MCPs are designed for broader uses that go beyond answering questions, but can be useful in contexts where having up-to-date data is more important than comprehensive answers.

Follow the [dedicated MCP guide](/guides/ai/mcp) if you want to implement it in your application.