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English
$
USD
I will test and improve your rag pipeline accuracy
Mazu
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About this gig
Your LLM might sound confident. But is it actually correct?
I evaluate and benchmark your LLM or RAG system for accuracy,
hallucination rate, and response quality.
WHAT I MEASURE:
Accuracy Does the AI give correct answers?
Faithfulness Are responses grounded in source data?
Relevancy Does it actually answer the question?
Hallucination Rate How often does it make things up?
Context Precision Is the right info being retrieved?
Latency & Cost How fast and expensive per query?
PACKAGES:
BASIC ($100) Core accuracy testing, hallucination check,
quick report
STANDARD ($250) Full RAGAS evaluation, faithfulness,
relevancy, detailed report
PREMIUM ($400) Complete benchmark suite, CI/CD integration,
re-test, optimization plan
️ TOOLS: RAGAS, DeepEval, TruLens, Custom Python eval scripts,
LLM-as-a-Judge
️ WHY THIS MATTERS:
Hallucinations destroy user trust instantly
Bad RAG retrieval wastes 60%+ of your token budget
Investors require accuracy benchmarks before funding
You can't improve what you don't measure
Message me before ordering for a free initial assessment.
Limited: 15 clients per month.
Get to know Mazu
Mazu
"I Protect Your AI From Security Risks, Bias Compliance Failures"
- FromPakistan
- Member sinceNov 2025
- Avg. response time1 hour
Languages
Urdu, English
I'm an AI Security & Trust Engineer passionate about making
AI safe, reliable, and legally compliant. I help businesses
deploy production-ready AI systems that don't hallucinate,
leak data, or fail audits.
My work spans AI red teaming, building LLM guardrails,
evaluating agents, developing custom RAG pipelines, and
creating EU AI Act/NIST compliance documentation.
I use tools like LangChain, RAGAS, PyRIT, Pinecone, and
LlamaGuard to ensure your AI is secure, accurate, and ready
for enterprise.
Message me to discuss your project.
My Portfolio
FAQ
What's the difference between this and Red Teaming?
Red Teaming tests for security vulnerabilities (attacks, injections). This gig tests for quality (accuracy, hallucinations, relevance).
What is RAGAS?
RAGAS is the industry-standard framework for evaluating RAG (Retrieval-Augmented Generation) systems. It measures faithfulness, answer relevancy, and context precision
Do you test custom fine-tuned models?
Yes. I can evaluate GPT-4, Claude, Gemini, LLaMA, Mistral, and any custom fine-tuned model.
How many test queries do you run?
Basic: 50 queries. Standard: 200 queries. Premium: 500+ queries with a custom evaluation dataset.
What is "LLM-as-a-Judge"?
It's a technique where a highly-reliable LLM (like GPT-4) grades your model's responses against a rubric. It's the industry standard for automated evaluation.
Can you test my RAG system's retrieval quality?
Yes. I measure context precision (did it find the right docs?) and context recall (did it miss anything important?).
What deliverables do I receive?
A professional PDF report with metric scores, benchmark comparisons, hallucination analysis, and prioritized improvement recommendations.
Do you set up automated testing?
The Premium package includes setting up a CI/CD evaluation pipeline using GitHub Actions + DeepEval/RAGAS that runs automatically on every code change.
How do I know if my hallucination rate is acceptable?
Industry benchmark is <5% for production systems. I'll compare your results against industry standards and tell you exactly where you stand.
What do you need to start?
Access to your LLM/RAG system (URL, API, or repo), a description of its use case, and any existing test datasets if you have them.
