General vs Domain-Specific AI
General-purpose LLMs like GPT-4 are trained on broad internet data. They perform well on common tasks but lack deep expertise in specialised domains — medical diagnosis, legal document analysis, or proprietary engineering terminology. Fine-tuning adjusts the model’s weights on your specific dataset.
Fine-Tuning vs RAG vs Prompting
- Prompt Engineering: Fastest to implement. Best for general tasks. No training required.
- RAG (Retrieval-Augmented Generation): Injects relevant documents into the context at runtime. Best for knowledge bases that change frequently.
- Fine-Tuning: Trains the model on your data. Best for consistent tone, format, and domain-specific reasoning.
Cost Consideration Fine-tuning requires significant compute and data preparation. For most business use cases, RAG combined with strong system prompts delivers 80% of the benefit at 10% of the cost.