I will build a rag evaluation harness with grounded quality metrics


Über diesen Service
I will build a reproducible evaluation workflow for your retrieval-augmented generation system. The work can measure retrieval quality, answer groundedness and response quality against a representative evaluation set.
You will receive Python source code, configuration handling, clear metrics, a machine-readable results file, a concise findings report, automated tests for core paths and a setup guide. Standard and Premium packages include a reusable harness that can be rerun as your prompts, documents or models change.
Before ordering, please share the RAG architecture, a small representative question set, expected source documents and any documented API interface. Use non-production credentials only when an integration is required.
This Gig excludes model fine-tuning, production deployment, regulated decisions, ongoing support and access to confidential production credentials. Scope is agreed before work begins.
Lerne Aditya P kennen
Snr Applied ML Researcher
- AusAustralien
- Mitglied seitAug. 2026
- ⌀ Antwortzeit1 Stunde
Sprachen
Englisch
FAQ
What do you need from me before starting?
A representative question set, expected source documents, your current RAG architecture and any documented API interface. Please use non-production credentials only.
Which metrics will the harness include?
Metrics are selected for your data and architecture. Typical options include retrieval hit rate, ranking quality, answer groundedness, citation coverage and reproducible summary statistics.

