AI RAG Consulting
Consulting for RAG system design and implementation.
Cassandra Research editorial team
Research methodology: Check the cited research, model documentation and evaluation conditions before applying an AI technique to professional work.
Overview
RAG (Retrieval Augmented Generation) is the architecture of choice for professional AI systems that require accuracy and source citation. Our RAG consulting helps organisations design, build, and optimise RAG systems that deliver reliable, grounded AI outputs.
RAG Consulting Scope
- •Knowledge base design and curation strategy
- •Vector database selection and implementation
- •Embedding model selection and optimisation
- •Retrieval pipeline design and tuning
- •LLM integration and prompt architecture
- •Evaluation framework and accuracy testing
Frequently Asked Questions
Why choose RAG over fine-tuning?
RAG supplies external documents at query time, which can help with traceability and updates. Fine-tuning can adapt model behaviour. Choose through evaluation of your task and data; neither approach guarantees accurate or current responses.
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