instruction: Create a Dockerfile for orchestration
output: ```dockerfile
  from trusted databases, ensuring higher accuracy and compliance with industry standards. This mitigates risks such as misinformation or regulatory violations.Enhancing cost efficiency and performanceTraining and fine-tuning large AI models can be computationally expensive and time-consuming. However, integrating RAG provides:Reduced fine-tuning needs: Instead of retraining an AI model every time new data emerges, RAG allows the model to fetch and incorporate updated information dynamically.Better performance with smaller models: With the right retrieval techniques, even compact AI models can perform well by leveraging external knowledge efficiently.Lower operational costs: Instead of investing in expensive infrastructure to support large-scale retraining, businesses can optimize resources by utilizing RAGâs real-time retrieval capabilities.By following this guide, you now have the foundational knowledge to implement RAG with Neo4j, enabling your AI system to deliver more accurate, relevant, and insightful responses. The next step is experimentationâchoose a dataset, configure your stack, and start enhancing your AI with the power of retrieval-augmented generation.
  Request changesTable of contentsIntroductionUnderstanding RAGIntroduction to Graph databasesPractical implementation: testing RAG effectivenessTeaching AI about new technologiesSetting up GenAI stack with GPU acceleration on LinuxGPU poweredCPU poweredSetting up on other platformsInitial startupMonitoring progressWhen to leverage RAG for optimal results
  
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