Enterprise adoption of large language models (LLMs) often comes down to a critical design choice: retrieval-augmented generation (RAG) or fine-tuning? At YASHICOM, we help organisations evaluate both approaches so that solutions are cost-effective, maintainable, and aligned with data governance.
What is RAG?
RAG keeps the base model unchanged and augments it with your own data at query time. A retrieval system fetches relevant documents or knowledge, and the LLM generates answers grounded in that context. YASHICOM has implemented RAG pipelines for clients who need up-to-date, domain-specific answers without retraining models.
What is fine-tuning?
Fine-tuning updates the model’s weights on your data, so the model internalises your domain’s language and tasks. It can improve accuracy and consistency for narrow use cases. YASHICOM’s technical teams support fine-tuning when clients have sufficient high-quality data and a clear, stable task definition.
When YASHICOM recommends which approach
We typically recommend RAG when: knowledge changes frequently, you need strong traceability to sources, or you want to avoid ongoing retraining. We recommend fine-tuning when: the task is well-defined, you have large volumes of labelled data, and you need the model to adopt a specific style or schema.
Many of our clients use a hybrid: a RAG layer for factual, updatable knowledge and light fine-tuning for tone or format. YASHICOM’s architects design these systems so they remain scalable and governable.
Getting started
If you are exploring LLM deployment for your organisation, YASHICOM can help you compare RAG and fine-tuning against your data, compliance, and performance requirements. Contact us to discuss your use case.