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I will evaluate your rag or llm application with ragas and deepeval
Hilal A.
About this gig
Is your RAG or LLM application producing irrelevant, inconsistent or unsupported answers?
I will evaluate your existing application, identify where it fails and provide practical recommendations for improvement.
Depending on the selected package, the assessment may cover:
- Retrieval quality
- Context relevancy
- Answer relevancy
- Faithfulness and grounding
- Hallucination risks
- Prompt behavior
- Insufficient-context handling
- Recurring failure patterns
- Ragas or DeepEval metrics
- Prioritized technical improvements
You will receive more than a list of scores. I will explain the observed problems, likely causes and recommended next steps.
I work with Python-based RAG and LLM systems using LangChain, LangGraph, Qdrant, ChromaDB, FAISS, Langfuse, Ragas and DeepEval.
This Gig evaluates an existing application. Building a new RAG system or implementing major architectural changes requires a separate order.
Please contact me before ordering and share your architecture, available test data and current problems.
Get to know Hilal A.
Hilal A.
AI Engineer for RAG AI Agents and MLOps
- FromTurkey
- Member sinceNov 2024
- Avg. response time1 hour
Languages
English, Turkish
Hi, I'm Hilal, an AI/ML Engineer specializing in RAG, AI agents, LLM applications and MLOps. I build reliable AI systems with Python, FastAPI, LangGraph, LangChain, Qdrant, Docker and Kubernetes. I can help with RAG pipelines, multi-agent workflows, LLM integrations, evaluation, observability, API development, deployment and data pipeline optimization. I focus on maintainable code, clear documentation and practical solutions that work beyond the prototype stage. Message me to discuss your project.
FAQ
What do you need to evaluate my application?
I need a description of the application, its expected behavior, representative queries and access to the relevant code, API, test environment or exported query-context-answer results.
Can you evaluate my system without production access?
Yes. I can work with a local repository, source archive, test API, development environment or exported evaluation samples.
What is a test case?
A test case usually includes a user query, the retrieved context, the generated answer and, when available, the expected behavior or reference answer.
Can you create the evaluation dataset?
Standard and Premium can include a structured dataset based on the examples and expected behavior provided by the client.
Will you use Ragas or DeepEval?
Yes, when the application data and evaluation scope are suitable. Not every metric is appropriate for every system, so the final metric set is selected according to the use case.
Will you fix every issue you identify?
No. Basic and Standard focus on evaluation and recommendations. Premium includes only small, previously agreed improvements. Major implementation work requires a custom offer.
Can you guarantee a specific score improvement?
No. Results depend on the data, retrieval architecture, models and permitted changes. I do not guarantee a specific numerical improvement.
Can you guarantee that the system will be hallucination-free?
No LLM system can be guaranteed to be completely hallucination-free. The evaluation can identify unsupported answers and recommend controls to reduce the risk.
Can you evaluate an agentic application?
Yes. The package scope depends on the number of agents, tools and LLM components included in the request path.
