Retrieval-Augmented Generation (RAG)
Retrieval-Augmented Generation (RAG) is an architectural approach that improves the accuracy and reliability of LLM applications by grounding their outputs on external data....
Eugene Slyvinsky
RAG: Deployment and Operations in Production
The final step is deploying the RAG system so that end-users or applications can consume it, and setting up an operational workflow to maintain it. We’ll discuss various deployment architectures and considerations on AWS, using NVIDIA and Databricks components....
RAG: Performance Optimization with NVIDIA TensorRT and Quantization
Large Language Models and semantic search can be resource-intensive. To deploy a responsive RAG system at scale, we need to optimize both the retrieval and generation components for latency and efficiency....
RAG: Post-Processing & Evaluation
Once the LLM generates an answer based on retrieved documents, there are a few additional steps that can enhance the quality and reliability of the RAG system: Answer Post-Processing, Evaluation and Continuous Monitoring....
RAG: LLM Integration and Generation
When it comes the generative part – using a Large Language Model to produce the final answer, augmented with the retrieved context. We need to integrate an LLM into our pipeline....
RAG: Query Handling and Document Retrieval Workflow
When a user query comes into a RAG system it must retrieve relevant documents and prepare them to feed into the LLM....