Fine-Tuning vs. RAG
Two complementary approaches for adapting AI to domain-specific knowledge: fine-tuning trains the model on new data, while RAG retrieves relevant information at inference time.
Fine-tuning modifies the model's weights to internalize domain knowledge, terminology, and task-specific behavior. It is most effective for learning consistent output formats, domain-specific language patterns, and task-specific reasoning styles. However, fine-tuned knowledge becomes stale as documents change, and fine-tuning requires significant compute, labeled data, and ML expertise.
RAG keeps model weights frozen and instead retrieves relevant passages at query time from an up-to-date knowledge base. It handles dynamic content (contracts that change, regulations that update), enables source citations (the retrieved passages are the explicit basis for the answer), and is far easier to maintain and update. For document intelligence applications where the content changes frequently and verifiable citations are required, RAG is the preferred architecture. Fine-tuning is most valuable as a complement — teaching the model how to format legal or financial analysis — rather than a replacement for retrieval-based grounding.
Related Terms
More ai/ml Terms
Retrieval-Augmented Generation (RAG)
An AI architecture that combines information retrieval with text generation to produce answers grounded in source documents.
Vector Embedding
A numerical representation of text as a high-dimensional vector, enabling semantic similarity comparisons between passages.
BM25
A probabilistic keyword-ranking algorithm that scores documents by term frequency and inverse document frequency.
Chunking
The process of splitting large documents into smaller, overlapping segments optimized for retrieval and embedding.
Hallucination
When an AI model generates plausible-sounding but factually incorrect or fabricated information.
Large Language Model (LLM)
A neural network trained on massive text corpora that can understand and generate human language.
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