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RAG Context Retrieval Pipeline is a medium coding interview question covering nlp, vector search, machine learning — the kind asked at FAANG (Facebook/Meta, Apple, Amazon, Netflix, Google) and AI-first companies like Anthropic, OpenAI, and Nvidia.
Implement the retrieval step of a Retrieval-Augmented Generation (RAG) pipeline.
Given: a list of documents, a query string, and k.
Steps: 1. Compute TF-IDF vectors for all documents 2. Compute TF-IDF vector for the query 3. Rank documents by cosine similarity to query 4. Return the top-k most relevant documents
The retrieved documents are then passed as context to an LLM (e.g., Claude, GPT) to generate a grounded answer.
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