What is RAG Management?
RAG (Retrieval-Augmented Generation) Management is the practice of optimizing AI systems that combine retrieval-based search with generative models. It ensures that models fetch the most relevant, up-to-date information from external databases and integrate it seamlessly into outputs, improving both accuracy and reliability.
Key Components:
- Retrieval Optimization: Fine-tunes how data is fetched from internal and external knowledge bases.
- Data Pipeline Governance: Oversees how data is ingested, cleaned, and indexed for retrieval.
- Hybrid Training: Trains retrieval and generation components together to reduce hallucinations and increase coherence.
- Latency Reduction: Speeds up data retrieval without sacrificing the quality of AI outputs.
Applications:
- Building enterprise knowledge assistants that answer employee questions using internal documents.
- Developing academic research tools that generate literature summaries with citations.
- Automating customer support with sourced, accurate answers from product databases.
.svg)
