, I discuss a selected step of the RAG pipeline: The document retrieval step. This step is critical for any RAG system’s performance, considering that without fetching essentially the most relevant documents, it’s difficult...
, I walked through constructing an easy RAG pipeline using OpenAI’s API, LangChain, and native files, in addition to effectively chunking large text files. These posts cover the fundamentals of organising a RAG pipeline...
, I saw our production system fail spectacularly. Not a code bug, not an infrastructure error, but simply misunderstanding the optimization goals of our AI system. We built what we thought was a elaborate...
engineering is some of the relevant topics in machine learning today, which is why I’m writing my third article on the subject. My goal is to each broaden my understanding of engineering contexts...
, I walked you thru organising a quite simple RAG pipeline in Python, using OpenAI’s API, LangChain, and your local files. In that post, I cover the very basics of making embeddings out of...
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RAG, which stands for Retrieval-Augmented Generation, describes a process by which an LLM (Large Language Model) could be optimized by training it to tug from a more specific, smaller knowledge base relatively than its...
generate tons of words and responses based on general knowledge, but what happens when we'd like answers requiring accurate and specific knowledge? Solely generative models often struggle to offer answers on domain specific...
I explain find out how to construct an app that generates multiple alternative questions (MCQs) on any user-defined subject. The app is extracting Wikipedia articles which are related to the user’s request and...