RAG vs Fine-tuning: What Works Best for Construction
Comparing AI training approaches for construction regulations. Why we chose 3-tier RAG.
When building an AI system for construction, the key question is how to teach the model to understand the regulatory framework. Let's consider two main approaches.
Fine-tuning: Training on Data
Fine-tuning involves retraining the base LLM on construction documents. Pros: fast responses, no dependency on external sources. Cons: data staleness, high retraining cost, hallucinations.
RAG: Retrieval + Generation
RAG (Retrieval-Augmented Generation) searches for relevant fragments in the knowledge base and passes them to the model as context. Always up-to-date data, transparent sources, minimal hallucinations.
Our Choice: 3-Tier RAG
Stroyintel uses a combination of ApeRAG (adaptive search), GraphRAG (knowledge graph of regulatory relationships), and Fine-tuned LLM for final generation. This achieves >95% accuracy on construction tasks.