Rag

Model Drift Model drift is the general phenomenon where a deployed model’s predictive performance degrades over time, even though nothing about the model itself has changed. The model is the same; the world it operates in isn’t. Taxonomy Drift is usually classified by what’s shifting in the underlying probability distributions. Data drift (covariate shift) The distribution of input features P(X) changes, but the relationship P(Y|X) stays the same. A fraud detection model starts seeing a higher fraction of mobile-wallet payments — inputs look different, but the rules for “is this fraud” haven’t changed. ...

Top-K in RAG Search In Retrieval-Augmented Generation (RAG), top-k is the number of most relevant document chunks the retriever returns from the vector store for a given query. The “k” is literally just a number — top-3, top-5, top-10, etc. How it works Embed the query into a vector Run a similarity search (cosine, dot product, etc.) against indexed chunks Retriever ranks every chunk by similarity score Top-k says “give me the k highest-scoring ones” Those chunks get stuffed into the LLM’s context as grounding material before generation Choosing k — the tradeoff Too low (k=1, 2): ...