Do I need RAG or fine-tuning for my use case?
RAG is the first architecture Aegis tests for many business use cases. Fine-tuning is for domain-specific output style or constrained-vocabulary requirements. Aegis defaults to RAG within the Truth Architecture framework.
The short answer.
RAG is the first architecture Aegis tests for many business use cases. Fine-tuning is for domain-specific output style or constrained-vocabulary requirements. Aegis defaults to RAG within the Truth Architecture framework.
This is a question Aegis hears regularly during discovery. Here is the practical way to frame it.
How Aegis approaches this.
Aegis Boardroom's answer is shaped by three frameworks. Truth Architecture: recommendations are designed to be source-traced. Confidence Contract: recommendations are mapped to the canonical Aegis confidence states (I Know / I Think / I'm Inferring / I Don't Know). Life Integrity Engine: recommendations that may increase irreversible-harm risk are flagged for refusal or human review, not softened.
The fastest path is the AI Readiness Assessment: it returns a confidence-mapped band for your specific situation. From there, the Quick Win Plan or a deeper engagement scopes the right paid Aegis next step.
Frequently asked questions.
Which one should I start with?
RAG, for most business use cases. It's the first architecture Aegis tests, because it grounds answers in your sources within the Truth Architecture framework.
When is fine-tuning actually the right call?
When you need a domain-specific output style or a constrained vocabulary. Those are the cases fine-tuning is built for; most business problems don't require it.
What's the difference in plain terms?
RAG works from your own sources, so the answers stay grounded in your material. Fine-tuning shapes the model's style or vocabulary up front. Aegis defaults to RAG and uses fine-tuning only when the output requirement calls for it.