Fine-Tuning vs Retrieval-Augmented Generation
Data-driven comparison powered by the gentic.news knowledge graph
Fine-Tuning
technology
Retrieval-Augmented Generation
technology
Ecosystem
Fine-Tuning
No mapped relationships
Retrieval-Augmented Generation
Fine-Tuning
Artificial intelligence is the capability of the computational systems to perform tasks typically associated with human intelligence, such as learning, reasoning, problem-solving, perception, and decision-making. Artificial intelligence has been used in applications throughout industry and academia.
Retrieval-Augmented Generation
Retrieval-augmented generation (RAG) is a technique that enables large language models (LLMs) to retrieve and incorporate new information from external data sources. With RAG, LLMs first refer to a specified set of documents, then respond to user queries. These documents supplement information from
Recent Events
Fine-Tuning
Fine-tuning is argued to be losing its potency as a unique differentiator in favor of data-first approaches
Retrieval-Augmented Generation
Enterprise trend report shows strong preference for RAG over fine-tuning for production AI systems
Practical guide published comparing RAG vs fine-tuning approaches
Article highlights 10 common evaluation pitfalls that can make RAG systems appear grounded while generating hallucinations
Basic RAG gained prominence as the go-to solution for enhancing LLMs with external knowledge
New study validates retrieval metrics as proxies for RAG information coverage