movchinarinFeedback IntelligenceBuild a RAG chatbot and optimize the performance through usageRAG is widely used to leverage LLMs in practical applications like chatbots. In this project, a RAG system was implemented using publicly…Sep 13Sep 13
movchinarinFeedback IntelligenceFeedback Collection Mechanisms for RAG and Prompt-Engineered Systems in ProductionWhen running RAG and prompt-engineered (PE) systems in production, gathering feedback is key to keeping these solutions accurate and…Aug 29Aug 29
movchinarinFeedback IntelligenceHow to define metrics that matter to your use-case-specific RAGWhen building a domain-specific chatbot or conversational AI, one of the main goals is to optimize the product to meet users’ needs — what…Aug 22Aug 22
movchinarinFeedback IntelligenceEnhancing LLMs with User-Driven Synthetic DataGarbage in, garbage out.Aug 15Aug 15
movchinarinFeedback IntelligenceOptimizing LLMs with RLHFReinforcement Learning with Human Feedback (RLHF) is a well-known technique in machine learning. It is essential for developing LLMs that…Jul 19Jul 19
movchinarinFeedback IntelligenceA RAG system backpacked with Feedback IntelligenceWell, we got to the point… we can take an example of RAG to apply all the theoretical knowledge discussed in the previous article.Jul 9Jul 9
movchinarinFeedback Intelligence(Part 2) The RAG Onion: Layered Analysis of System FailuresIn the previous article, we discussed the story of Root Cause Analysis (RCA) development, ie, from traditional software RCA to using…Jun 27Jun 27
movchinarinFeedback Intelligence(Part 1) RCA: The Evolution of Root Cause Analysis: From Traditional Software to Large Language…Root Cause Analysis (RCA) is crucial in software development for identifying and resolving core issues in applications. It follows a…Jun 21Jun 21