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$
USD
I will llm evaluation framework setup
Bharath
About this gig
Description:
Shipping an LLM feature without an eval suite is shipping blind. I design and build evaluation pipelines that tell you, with numbers, whether your prompts, model or RAG setup actually got better before your users do.
What you get:
- Golden dataset design (task-specific test cases, edge cases, adversarial inputs)
- Metric selection: exact match, semantic similarity, rubric-based LLM-as-judge, hallucination/faithfulness scoring, latency and cost
- Automated eval harness (Python; LangSmith, Promptfoo, Ragas, DeepEval or custom) wired into CI
- Regression dashboard and a clear ship / dont ship report
Please reach out to me before placing the error.
Get to know Bharath
Bharath
LLM AI Model Evaluation Specialist , QA automation Architect ,Eval pipelines
- FromIndia
- Member sinceSep 2021
- Avg. response time1 hour
- Last delivery8 months
Languages
English, Tamil, Hindi
I help AI product teams answer one question: is this model actually good enough to ship? I built evaluation pipelines for LLM generated reports, validated models
Test consultant with 15+ years of professional experience and has expertise in QA for AI agents, Chatbots, RAGS, Web and mobile applications in domain like Banking, Insurance, Manufacturing, ecommerce. etc
* Expertise in automation testing using tool like Playwright, Selenium using Python, Java
* API testing using Postman
* Performance testing using Jmeter and K6 tools
* Security testing and Vulnerability assessment (OWASP standards)
Other AI Development Services I Offer
FAQ
Do I need to share my production data?
No. I work from samples you choose, synthetic data, or a redacted export. Everything stays under your NDA and is deleted on delivery.
Which eval tools do you use?
Which eval tools do you use? Promptfoo, LangSmith, Ragas, DeepEval, or a plain Python/pytest harness. I pick what fits your stack; the code is yours either way.
Can you evaluate a RAG system or an agent, not just a single prompt?
Yes. For RAG I score retrieval and generation separately (context precision/recall, faithfulness, answer relevance). For agents I evaluate tool-call correctness and task completion.
What does "LLM-as-judge" mean and is it reliable?
A second model grades outputs against a rubric I write with you. I calibrate it against a human-labelled sample so you know how much to trust the scores.
Do you also do red-teaming or model comparison?
Yes — both are separate gigs on my profile, or add them as extras here.
