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$
USD
I will evaluate your rag application using ragas
Jay Telgote
About this gig
Are you looking to optimize your AI application or fix inaccurate outputs? A poorly tuned RAG application leads to high costs, slow latency, and severe LLM hallucination issues. I provide expert RAG evaluation and AI testing services to ensure your production pipeline delivers precise, context-aware answers.
Using industry-standard frameworks like Ragas, TruLens, and DeepEval, I run a comprehensive RAG assessment on your system. I analyze the entire LangChain or LlamaIndex pipeline, measuring core metrics: context relevance, faithfulness (hallucination checks), and answer relevance.
What you will get:
- Comprehensive RAG performance score report
- Deep evaluation of your vector database retrieval accuracy
- Automated synthetic test data generation for stress-testing
- Clear, actionable AI optimization strategies for chunking, embeddings, and prompt engineering
Stop guessing why your LLM application is underperforming. Lets benchmark your system, lower your token costs, and build user trust with a reliable, production-grade
Get to know Jay Telgote
Jay Telgote
AI Specialist
- FromIndia
- Member sinceJul 2022
Languages
English, Hindi
AI/ML Specialist, specializing in advanced AI solutions, focusing on retrieval augmented generation (RAG) and natural language processing (NLP).
Proficient in building and training AI models, fine-tuning large language models, and developing predictive models.
Core skills include Python, Elasticsearch, Neo4j, and Langchain, applied to create innovative data-driven applications that drive impactful results.
Feel Free to reach out!
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FAQ
What metrics do you use to evaluate the RAG application?
I measure the RAG Triad: Context Relevance (retrieval accuracy), Faithfulness (hallucination checks), and Answer Relevance (utility). I also evaluate context recall, precision, and latency using frameworks like Ragas or TruLens.
Do I need to share my source code or data?
For Basic, just send a CSV/JSON of queries and outputs. For Standard/Premium, reviewing your retrieval logic gives deeper insights. I am open to signing an NDA, or you can share sanitized/mock data and code.
Will you fix the issues you discover?
This gig focuses on diagnosing bottlenecks (like poor chunking or weak embeddings) and providing an optimization blueprint. I can implement the actual fixes and code adjustments for you as a custom gig extra.
What frameworks do you support?
I evaluate RAG applications built on any stack, including custom Python setups, LangChain, LlamaIndex, or enterprise platforms. The analysis works regardless of your specific vector database or LLM choice.
What is synthetic test data generation?
If you lack user queries, I use your source documents to programmatically generate hundreds of realistic test questions and ground-truth answers. This lets us thoroughly stress-test your RAG pipeline.
