Category:
Category:
Fine-Tuning vs RAG
Category:
Core AI & LLM Concepts
Definition
Comparison of modifying model weights (fine-tuning) versus injecting external knowledge (RAG).
Explanation
Fine-tuning adjusts model weights using labeled datasets to teach behaviors, styles, or domain patterns. RAG retrieves external documents and injects them into the LLM context at runtime. Fine-tuning is best for style, format, and reasoning changes. RAG is best for factual accuracy and dynamic knowledge. Most enterprise AI systems use a hybrid approach: fine-tuning for behavior and RAG for knowledge grounding.
Technical Architecture
Fine-Tuning → Updated Model Weights OR RAG → External Retrieval → Grounded LLM
Core Component
Training pipeline, retrieval layer, embeddings, vector DB, evaluation suite
Use Cases
Enterprise assistants, customer support, analytics automation, legal QA
Pitfalls
Fine-tuned models become stale; RAG fails with poor retrieval
LLM Keywords
RAG Vs Fine Tuning, Model Retraining Vs Retrieval
Related Concepts
Related Frameworks
• Embeddings
• Chunking
• Retrieval Pipelines
• Instruction Tuning
• Hybrid Architecture Model