--- title: "Recsys Pipeline Architect" type: skill id: "wshobson-agents/llm-engineering/recsys-pipeline-architect" category: "AI & machine learning" publisher: "Seth Hobson" license: "MIT" source: "https://github.com/wshobson/agents/tree/7e2fb55" canonical: "https://labs.pho.chat/en/skills/wshobson-agents/llm-engineering/recsys-pipeline-architect" open_in_pho: "https://pho.chat/customize?item=wshobson-agents%2Fllm-engineering%2Frecsys-pipeline-architect&utm_source=labs&utm_medium=skill-page&utm_campaign=skills-library" --- # Recsys Pipeline Architect Design composable recommendation, ranking, and feed pipelines using the six-stage Source→Hydrator→Filter→Scorer→Selector→SideEffect framework popularized by xAI's open-sourced X For You algorithm. Use when building any system that picks "the top K items for a (user, context)" — content feeds, search ranking, RAG rerankers, task prioritizers, notification triage, ad selection. Source: Seth Hobson (MIT), copied at a pinned commit. Free to use. Part of the Phở Skills library (https://labs.pho.chat/en/skills). Skill set: LLM apps and fine tuning (https://labs.pho.chat/en/skills/wshobson-agents/llm-engineering.md). ## What it covers - Overview - When to Use This Skill - The Six-Stage Framework - Why this exact order - Workflow When Invoked - Key Trade-offs to Surface - 1. Single score vs multi-action prediction - 2. Candidate isolation vs joint scoring - 3. Online vs offline batch - Hard Rules - Anti-Patterns - Common Use Cases - Content feed (Strapi v5 plugin, TypeScript) - RAG retrieval reranker (Python async) - Task prioritizer (FastAPI service) - Notification triage (offline-batch job) - Upstream ## Other skills in this set - [Embedding Strategies](https://labs.pho.chat/en/skills/wshobson-agents/llm-engineering/embedding-strategies.md): Select and optimize embedding models for semantic search and RAG applications. Use when choosing embedding models, implementing chunking strategies, or optimizing embedding quality for specific… - [Hybrid Search Implementation](https://labs.pho.chat/en/skills/wshobson-agents/llm-engineering/hybrid-search-implementation.md): Combine vector and keyword search for improved retrieval. Use when implementing RAG systems, building search engines, or when neither approach alone provides sufficient recall. - [LangChain & LangGraph Architecture](https://labs.pho.chat/en/skills/wshobson-agents/llm-engineering/langchain-architecture.md): Design LLM applications using LangChain 1.x and LangGraph for agents, memory, and tool integration. Use when building LangChain applications, implementing AI agents, or creating complex LLM workflows. - [LLM Evaluation](https://labs.pho.chat/en/skills/wshobson-agents/llm-engineering/llm-evaluation.md): Implement comprehensive evaluation strategies for LLM applications using automated metrics, human feedback, and benchmarking. Use when testing LLM performance, measuring AI application quality, or… - [RAG Implementation](https://labs.pho.chat/en/skills/wshobson-agents/llm-engineering/rag-implementation.md): Build Retrieval-Augmented Generation (RAG) systems for LLM applications with vector databases and semantic search. Use when implementing knowledge-grounded AI, building document Q&A systems, or… - [Similarity Search Patterns](https://labs.pho.chat/en/skills/wshobson-agents/llm-engineering/similarity-search-patterns.md): Implement efficient similarity search with vector databases. Use when building semantic search, implementing nearest neighbor queries, or optimizing retrieval performance. - [Vector Index Tuning](https://labs.pho.chat/en/skills/wshobson-agents/llm-engineering/vector-index-tuning.md): Optimize vector index performance for latency, recall, and memory. Use when tuning HNSW parameters, selecting quantization strategies, or scaling vector search infrastructure. - [Checkpoint Promotion](https://labs.pho.chat/en/skills/wshobson-agents/llm-engineering/checkpoint-promotion.md): Gate fine-tuned checkpoints with drift budgets, paired comparison, and forgetting checks before promotion. Use after a training run produces a checkpoint, when deciding whether a tuned model ships… - [Dataset Curation](https://labs.pho.chat/en/skills/wshobson-agents/llm-engineering/dataset-curation.md): Prepare, format, and validate datasets for supervised fine-tuning and preference training. Use when converting raw data into training format, applying chat templates, configuring sequence packing… - [Eval Harness First](https://labs.pho.chat/en/skills/wshobson-agents/llm-engineering/eval-harness-first.md): Build the evaluation harness that gates every fine-tuning run — golden sets, per-failure-mode graders, judge calibration, and base-model baselines. Use when starting a fine-tuning effort, when… - [Fine-Tuning Method Selection](https://labs.pho.chat/en/skills/wshobson-agents/llm-engineering/finetuning-method-selection.md): Decide whether to fine-tune at all, and route to the right method (SFT, DPO/ORPO/KTO, GRPO/RLVR, continued pretraining) and base model. Use when starting any fine-tuning effort, when unsure whether… - [GRPO & RLVR Training](https://labs.pho.chat/en/skills/wshobson-agents/llm-engineering/grpo-rlvr-training.md): Train reasoning and verifiable-task behavior with GRPO and reinforcement learning from verifiable rewards (RLVR). Use when task success is algorithmically checkable (math, code, tool calls… - [LoRA & QLoRA Recipes](https://labs.pho.chat/en/skills/wshobson-agents/llm-engineering/lora-qlora-recipes.md): Configure LoRA and QLoRA supervised fine-tuning with current best-practice hyperparameters. Use when writing or reviewing a LoRA/QLoRA training configuration, choosing rank/alpha/target modules, or… - [Preference Optimization](https://labs.pho.chat/en/skills/wshobson-agents/llm-engineering/preference-optimization.md): Align a fine-tuned model with preference data using DPO, ORPO, KTO, or SimPO. Use when preference pairs or thumbs-up/down feedback exist, when choosing between preference-optimization methods, or… - [Quantized Export](https://labs.pho.chat/en/skills/wshobson-agents/llm-engineering/quantized-export.md): Export a promoted fine-tuned model in the right deployment format — merged safetensors, LoRA-only, GGUF with imatrix, or FP8. Use after a checkpoint passes promotion, when choosing a quantization… - [Trace To Training Data](https://labs.pho.chat/en/skills/wshobson-agents/llm-engineering/trace-to-training-data.md): Convert evaluation traces and production logs into SFT examples and preference pairs. Use when graded traces or failure examples exist and need to become training data, when applying rejection… - [Vision-Language SFT](https://labs.pho.chat/en/skills/wshobson-agents/llm-engineering/vision-sft.md): Fine-tune vision-language models (VLMs) with supervised learning on image+text data. Use when adapting a VLM to a visual domain or task, configuring frozen-vision-tower LoRA, or debugging a VLM… - [ML Pipeline Workflow](https://labs.pho.chat/en/skills/wshobson-agents/llm-engineering/ml-pipeline-workflow.md): Build end-to-end MLOps pipelines from data preparation through model training, validation, and production deployment. Use when creating ML pipelines, implementing MLOps practices, or automating… ## Use it - In Phở: open https://pho.chat/customize?item=wshobson-agents%2Fllm-engineering%2Frecsys-pipeline-architect&utm_source=labs&utm_medium=skill-page&utm_campaign=skills-library and press Try, or add it so Phở loads it whenever a request matches. Phở works with your own API key (OpenAI, Anthropic, Google and others). - Full instructions (original SKILL.md, unchanged): https://labs.pho.chat/skills/raw/wshobson-agents/llm-engineering/skills/recsys-pipeline-architect/SKILL.md - Original files: https://github.com/wshobson/agents/tree/7e2fb55 (MIT). - Full machine-readable index: https://labs.pho.chat/skills.json