• METRIC
  • GAP
  • DATA
  • HUMAN

How to validate RAG retrieval accuracy with your AI

Connect this MCP to your agent to run automated comparisons between your model's output and your ground truth data.

Ask AI about this page

Short answer

How do I check if my RAG retrieval is actually working?

You feed your model's responses and your gold-standard documents into the evaluator to get specific overlap scores. This tells you exactly how much of the correct context your pipeline is actually capturing. You'll see the breakdown of linguistic similarity and content match below.

The evaluation outcomes.

Where your results land.

The AI processes your data to produce these specific results.

METRIC

Numerical similarity scores

The AI uses the evaluator tools to generate ROUGE and BLEU scores. You get a clear mathematical view of how close the generated text is to your source.

GAP

Contextual mismatches

The agent identifies where the retrieved context failed to support the answer. You'll see where the model hallucinated or missed key facts.

DATA

Comparison reports

The AI organizes the comparison between the machine output and the gold standard. You get a structured view of the overlap.

HUMAN

Manual review needed

The AI flags low-scoring pairs for you to inspect. You decide if a low score is a retrieval failure or just a stylistic difference.

The workflow

What your AI does when the data arrives.

Your agent handles the heavy lifting of comparing text strings against your reference files.

  1. Ingest reference data

    Your agent reads the gold-standard documents and the model's generated responses you want to test.

    calculate_rouge_bleu

  2. Calculate overlap

    The AI runs the text through the math engines to determine n-gram overlap and precision.

    calculate_rouge_bleu

  3. Identify discrepancies

    The agent compares the scores to find where the retrieval failed to provide the necessary information.

    calculate_rouge_bleu

  4. Summarize accuracy

    The AI compiles the scores into a readable summary of your pipeline's performance.

    calculate_rouge_bleu

Try it

Copy these to start.

Use these prompts to kick off the evaluation process.

Starting points

Replace the bracketed text with your actual file paths or data sources.

Native V8 Connector

You're all set. Choose your MCP client and follow the setup instructions.

Connector linkhttps://edge.vinkius.com/vk_preview_z0q7gwUAfzM0N3ZK3eZQCz4jX1vB3r37FG9LvR3T/mcp

Claude Desktop

Follow the steps below to connect in seconds.

  1. 1In Claude Desktop, open Settings → Connectors.
  2. 2Click “Add custom connector” and paste the connector link above as the remote MCP server URL.
  3. 3Click Add and start a new chat — Native V8 capabilities are ready to use.
Configuration · claude_desktop_config.jsonCopy
{
  "mcpServers": {
    "llm-rouge-bleu-evaluator-mcp": {
      "url": "https://edge.vinkius.com/vk_preview_z0q7gwUAfzM0N3ZK3eZQCz4jX1vB3r37FG9LvR3T/mcp"
    }
  }
}
Copy into chat04

  • Compare the answers in my 'test_results.json' against the gold standards in 'ground_truth.pdf' using the evaluator.

  • Run a ROUGE and BLEU check on the output from my recent retrieval test in the 'logs' folder.

  • Check the accuracy of the responses in this Google Sheet against my reference documentation.

  • Evaluate the similarity between the model's output in 'response_v1.txt' and the source text in 'context.txt'.

  • Claude
  • ChatGPT
  • Cursor
  • VS Code
  • Windsurf
  • Claude Code
  • JetBrains
  • Cline

Start here

Connect LLM ROUGE & BLEU Evaluator once, then ask.

Just link the MCP to your client. Your credentials stay encrypted, and you can start running evaluations immediately.

Connect LLM ROUGE & BLEU Evaluator to your AI

FAQ

How the evaluator behaves.

  • 01

    Can the AI fix my RAG pipeline automatically?

    No. The AI only calculates the scores and identifies the gaps. You have to manually adjust your chunking or embedding strategy.

  • 02

    Does the AI delete my original documents?

    No. The AI only reads your files to perform the comparison. It has no permission to modify or delete your source data.

  • 03

    What metrics does it actually use?

    It uses ROUGE and BLEU to measure how much the generated text overlaps with your gold-standard documents.

  • 04

    Can I use this with any AI client?

    Yes, as long as your client is MCP-compatible, like Claude, Cursor, or Windsurf.

  • 05

    Will it tell me why a score is low?

    It can identify if a score is low due to missing context, but the final interpretation of why a model failed is up to you.

  • More questions about LLM ROUGE & BLEU Evaluator? The Connector page answers them. See everything the LLM ROUGE & BLEU Evaluator Connector can do

Connect Native V8 to Claude, Cursor, ChatGPT & more