I will evaluate and test llm, rag and ai agent systems


About this gig
AI Chatbot, LLM & RAG Evaluation Services
I help businesses and developers evaluate, test, and improve AI-powered applications, including chatbots, LLM workflows, RAG systems, and AI agents.
Services include:
- LLM and chatbot evaluation
- RAG workflow assessment
- Prompt and response quality review
- Hallucination and accuracy analysis
- AI agent evaluation
- Improvement recommendations
- Responsible AI and governance considerations
What you will receive:
- Clear evaluation findings
- Identified issues and risks
- Actionable improvement recommendations
- Professional summary report
Whether you are building a chatbot, knowledge assistant, AI agent, or GenAI application, I will help you understand its strengths, weaknesses, and opportunities for improvement.
Please contact me before placing an order to discuss your requirements.
Get to know Suganya P
GenAI Systems Engineer AI Evaluation and Governance Support
- FromIndia
- Member sinceAug 2025
- Avg. response time1 hour
Languages
Tamil, English
My Portfolio
FAQ
What types of AI systems do you evaluate?
I evaluate AI chatbots, LLM applications, RAG systems, AI agents, prompt workflows, and GenAI solutions.
Will you provide recommendations for improvement?
Yes. Every evaluation includes practical recommendations to improve quality, reliability, and user experience.
Do you work with custom AI solutions?
Yes. I can review custom AI applications, internal tools, and business-specific GenAI workflows.
Do you sign NDAs?
Yes. I can work under NDA for confidential projects.
Why is AI evaluation necessary, and how does it help improve my project?
AI evaluation helps identify hallucinations, retrieval issues, prompt weaknesses, bias, and inconsistent responses. The findings provide actionable recommendations to improve accuracy, reliability, user experience, and overall AI system performance.
Why should I evaluate my AI chatbot, LLM, or RAG system?
Evaluation helps uncover accuracy issues, hallucinations, retrieval problems, and response inconsistencies. The findings provide clear recommendations to improve performance, reliability, and user experience before deployment or scaling.

