recsys-pipeline-architect

A ranking pipeline organizes item processing into six composable stages: Source, Hydrator, Filter, Scorer, Selector, and SideEffect. This structure supports multi-source retrieval, metadata enrichment, ordered filtering, chained scoring, and top-K selection.

The best way to structure a content feed ranking system is using a multi-stage pipeline with parallel retrieval and hydration, ordered filtering, and asynchronous side effects. This prevents tangled logic by separating candidate sourcing, scoring, and final top-K selection into distinct stages.

Yes, this ranking pipeline architecture applies to RAG retrieval reranking. The Scorer and Selector stages rerank retrieved candidate documents and extract the top K most relevant items for the user context.

Build a notification triage system by routing alerts through ordered filtering, chained scoring, and top-K selection stages. This pipeline structure prioritizes urgent notifications while dropping irrelevant candidates before final output.

Yes, this candidate selection pipeline supports parallel retrieval and hydration alongside asynchronous side effects. The six-stage workflow processes multiple candidate sources concurrently and handles post-selection actions asynchronously.

Avoid using a top-K ranking pipeline when tasks require strict sequential dependencies or lack distinct scoring criteria. The architecture relies on composable stages, parallel retrieval, and chained scoring to deliver effective top-K selection.