iMerit RAG fine-tuning optimizes retrieval augmented generation models by refining their ability to leverage external knowledge bases, enhancing the relevance and accuracy of generated responses.
Process and consolidate your existing knowledge base and new data to create unique, context-aware answers, instructions, or explanations in human-like language rather than just summarizing the retrieved data. Deliver more accurate result with greater efficiency for better outcomes.
iMerit’s Ango Hub enables workflow customization and expert human feedback to build knowledge graphs for enhancing RAG models. Domain experts provide the human feedback needed to produce high-quality data for RAG fine tuning, while the platform applies automation to every step of the process to ensure efficiency and scale.
"iMerit red teaming integrated new data into our RAG chatbot at 72% reduced cost."
Principal Product Manager
To ensure their knowledge graph database contained correct information, this US technology company consulted iMerit on how to audit, implement, and test a new medical dataset to ensure safe and accurate medical advice for users.
GOAL: Develop a RAG-based agent to answer consumer health-related questions.
RAG Fine-Tuning Workflow:
Domain Experts: Two US-based triage nurses, one physician
Utilize reinforcement learning from human feedback with iMerit domain experts to fine-tune and improve model performance
Enhance data corpus by adding diverse, high-quality, and contextually relevant data to improve model accuracy, robustness, and performance.
Improve the precision of your large language model by creating a diverse set of prompts and response pairing.
The need for generative AI training data services has never been greater. iMerit combines the best of predictive and automated technology with world-class subject matter expertise to deliver the data you need to get to production, fast.
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