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RAG Quality Evaluator
x402
RAG Quality Evaluator is a paid API for AI agents from rag-quality-evaluator.johntaylor1online.workers.dev, paid per call via x402, $0.5/call, status unknown (last checked 2026-10-18).
Evaluates the quality of Retrieval-Augmented Generation (RAG) pipeline outputs by analyzing input-output pairs
Price / call$0.5
Success—
StatusUnknown
How this service works
RAG Quality Evaluator
Output
Returns a quality evaluation report for the provided RAG input-output pair, likely including scores for metrics such as faithfulness, relevance, groundedness, or coherence of the generated response relative to the retrieved context.
Example use cases
RAG chatbot response quality check
“I have a customer support RAG chatbot and I want to check if its answer is any good — the user asked 'How do I reset my password?', the retrieved docs said 'Click Forgot Password on the login page', and the bot responded 'To reset your password, click the Forgot Password link on the login screen.' Can you evaluate that response quality?”
Benchmarking RAG pipeline accuracy
“I'm comparing two RAG pipeline versions and need quality scores for their outputs. The input question was 'What are the symptoms of diabetes?', the retrieved context was 'Common symptoms include frequent urination, excessive thirst, and blurred vision', and the generated answer was 'Diabetes symptoms include increased urination and thirst.' Can you score this output?”
Detecting hallucination in RAG output
“I'm worried my RAG system is hallucinating — the question was 'Who founded Apple?', my retrieval returned 'Apple was founded by Steve Jobs, Steve Wozniak, and Ronald Wayne in 1976', but the generated response said 'Apple was founded by Steve Jobs and Bill Gates in 1975.' Please evaluate the quality and faithfulness of this response.”
Technical details
Endpoint
POSThttps://rag-quality-evaluator.johntaylor1online.workers.dev/evaluate?utm_source=zero.xyzWhen to prefer
Choose this endpoint when you need an automated, per-call quality assessment of RAG pipeline outputs without building your own evaluation framework. It is ideal for spot-checking individual RAG responses, integrating quality gates into agent workflows, or comparing RAG system versions. Prefer this over general LLM-as-judge prompts when you want a standardized, pay-per-use evaluation service with consistent scoring methodology.
Side effects
- Missing required 'input' or 'output' fields returns a 400 validation error
- Malformed JSON body causes request rejection
- Empty or null context/query fields may result in low-confidence evaluation scores
- Service unavailability due to Cloudflare Workers cold start or quota limits
- Payment failure via x402 protocol blocks request processing
Input
Schema · Request body
| Field | Type | Description |
|---|---|---|
| inputrequired | object | |
| outputrequired | object |
Tags
x402
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