---
title: "How to Build a RAG Application Using Claude?"  
description: "How to Build a RAG Application Using Claude?"  
author: "Manish Kumar"  
published: 2026-06-22  
updated: 2026-06-25  
canonical: https://answers.mindstick.com/qa/116854/how-to-build-a-rag-application-using-claude  
category: "artificial-intelligence"  
tags: ["artificial intelligence", "claude ai"]  
reading_time: 4 minutes  

---

# How to Build a RAG Application Using Claude?

## How to Build a RAG Application Using Claude?

## Answers

### Answer by Anubhav Sharma

Build it in one of two patterns:

1. **Classic vector RAG**: chunk your documents, create embeddings, store them in a vector DB, retrieve the top matches, then send those snippets to Claude. [Anthropic’s docs](https://docs.anthropic.com/en/docs/build-with-claude/embeddings) say Claude itself does **not** provide an embedding model, and recommend using an embeddings provider such as Voyage AI for vector search.
2. **Search-results RAG**: have your retrieval layer return `search_result` blocks to Claude, and Claude can produce **natural citations with source attribution** from those results. Anthropic documents this both for tool-returned results and for top-level content you already fetched.

For most production apps, I would use **vector retrieval for recall**, then pass the best snippets into Claude as **search results** so the answer is easier to cite and verify. Anthropic explicitly recommends RAG when prompts would otherwise get too large, because it reduces cost, latency, and context-window pressure.

### Recommended architecture

## Ingestion

- Load documents.
- Chunk them into small, semantically coherent pieces.
- Create embeddings with Voyage AI or another embedding provider. [Anthropic’s docs](https://docs.anthropic.com/en/docs/build-with-claude/embeddings) note that Claude does not ship its own embedding model.
- Store vectors plus metadata in your DB.

## Retrieval

- Embed the user question.
- Retrieve top-k chunks.
- Optionally rerank them.
- Send only the most relevant chunks to Claude.

## Generation

- Wrap the retrieved chunks as `search_result` blocks.
- Ask Claude to answer using only those sources.
- Enable citations so Claude can cite the retrieved text. Anthropic’s search-results feature supports this and is designed for RAG use cases.

## Performance

- Use **prompt caching** for repeated prefixes like system instructions, policies, and stable retrieved context. Anthropic says prompt caching reduces cost and latency and supports automatic caching across active Claude models.

### Minimal Python example

```python
# Import the Claude SDK client and the content block types used for RAG.
from anthropic import Anthropic
# Import the helper types for messages and search results.
from anthropic.types import MessageParam, TextBlockParam, SearchResultBlockParam

# Create the Anthropic client with your API key configured in the environment.
client = Anthropic()

# Pretend this function retrieves the top matching chunks from your vector DB.
def retrieve_top_chunks(question: str):
    # Search your embeddings index here.
    # Return chunks with source metadata, titles, and text.
    return [
        {
            "source": "https://docs.example.com/billing",
            "title": "Billing Guide",
            "text": "Invoices are generated on the first day of each month...",
        },
        {
            "source": "https://docs.example.com/refunds",
            "title": "Refund Policy",
            "text": "Refunds are available within 14 days for eligible plans...",
        },
    ]

# Get the user's question.
question = "How do monthly invoices and refunds work?"

# Retrieve the best supporting passages.
chunks = retrieve_top_chunks(question)

# Convert retrieved passages into Claude search_result blocks.
search_results = []
for chunk in chunks:
    # Each search result needs source, title, and a text content block.
    search_results.append(
        SearchResultBlockParam(
            type="search_result",
            source=chunk["source"],
            title=chunk["title"],
            content=[
                TextBlockParam(
                    type="text",
                    text=chunk["text"],
                )
            ],
            citations={"enabled": True},
        )
    )

# Ask Claude to answer using the retrieved evidence.
response = client.messages.create(
    model="claude-opus-4-8",
    max_tokens=800,
    messages=[
        MessageParam(
            role="user",
            content=[
                # Provide the retrieved evidence first.
                *search_results,
                # Then ask the actual question.
                TextBlockParam(
                    type="text",
                    text=question,
                ),
            ],
        )
    ],
)

# Print Claude's answer.
print(response)
```

### Practical tips

Use **small chunks**, clear **source URLs**, and descriptive **titles** so citations stay precise. Anthropic notes that breaking long content into logical text blocks gives Claude finer citation boundaries.

Use **tool-based search results** when retrieval is dynamic, and **top-level search results** when you already fetched or cached the content. Anthropic documents both patterns.

If your app needs **real-time facts** like account balances or live policy data, retrieval alone may not be enough; Anthropic recommends **tool use** for live system lookups in those cases.

### The simplest mental model

Claude is the **reasoning layer**, not the vector store. Your retrieval stack finds the evidence; Claude turns that evidence into a grounded answer with citations. Anthropic’s current docs are aligned around that architecture.


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Original Source: https://answers.mindstick.com/qa/116854/how-to-build-a-rag-application-using-claude

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