Knowledge Graphs & LLMs: Fine-Tuning vs. Retrieval-Augmented Generation - Graph Database & Analytics

Discover the limitations of Large Language Models (LLMs), and how to overcome them through fine-tuning vs. retrieval-augmented generation.

Fine-tuning vs. RAG for LLMs Skip to content Neo4j to acquire GraphAware, launch new open-standards intelligence analysis solutions | Read more Menu Search Close Menu Products FULLY-MANAGED AuraDB Store and query connected data at scale Virtual Graph Create and query a knowledge graph on existing data Aura Graph Analytics Run graph algorithms on any data, any cloud Aura Agent Build and deploy context-aware agents fast SELF-MANAGED Graph Database Store connected data with a graph database Graph Data Science Run graph algorithms on connected data Enterprise Studio Securely query, explore, and vi

related reading

• Patterns for Building LLM-based Systems & Productseugeneyan.com
• Knowledge graph vs. vector database for grounding your LLMneo4j.com
• The GraphRAG manifesto: Adding knowledge to GenAIneo4j.com
• What We’ve Learned From A Year of Building with LLMs – Applied LLMsapplied-llms.org
• Advanced RAG Techniques: What They Are & How to Use Themfalkordb.com
• Retrieval-Augmented Generation for Large Language Models: A Surveyarxiv.org
• Towards Data Sciencetowardsdatascience.com
• The Ultimate Guide to Fine-Tuning LLMs from Basics to Breakthroughs: An Exhaustive Review of Technologies, Research, Best Practices, Applied Research Challenges and Opportunities (Version 1.0)arxiv.org
• Fine-Tuning Llama-2: Tailoring Models to Unique Applicationsanyscale.com
• Building RAG-based LLM Applications for Productionanyscale.com
• What is Retrieval Augmented Generation (RAG)? | Databricksdatabricks.com
• Code a simple RAG from scratchhuggingface.co