rag-pipeline-architect

Name: rag-pipeline-architect
Availability: InStock
Author: vilia

Design and optimize production-grade RAG pipelines from ingestion to response generation.

Updated Mar 12, 2026

Search Tags:#architecture #rag #evaluation #retrieval #chunking #embedding #rag-pipeline

Ready to run

npx skills add https://github.com/viliawang-pm/ai-engineering-toolkit --skill rag-pipeline-architect

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: rag-pipeline-architect
Source: https://github.com/viliawang-pm/ai-engineering-toolkit/tree/main/skills/rag-pipeline-architect
Command: npx skills add https://github.com/viliawang-pm/ai-engineering-toolkit --skill rag-pipeline-architect

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

Design production-grade RAG pipelines from document ingestion to retrieval-augmented response generation, with systematic evaluation and optimization at every stage.

Core Features & Use Cases

Tiered RAG architectures (Naive, Advanced, Modular) to cover end-to-end deployment
Guided workflow from ingestion, chunking, embedding, indexing, retrieval, and generation
Evaluation harness design and deployment strategies for robust QA and performance

Quick Start

Design and implement a modular RAG pipeline using your documents, embeddings, and a vector store.

Frequently Asked Questions about rag-pipeline-architect

High-intent search queries and answers about installing and using this skill.

FAQPage Schema

How do I design a production-grade RAG pipeline from document ingestion to response generation?▼

Design a production-grade RAG pipeline by following a guided workflow covering document ingestion, chunking, embedding, indexing, retrieval, and response generation, supported by tiered architectures and systematic evaluation harnesses.

What's the best way to choose embedding models and chunking strategies for my RAG system?▼

Choose embedding models and chunking strategies by evaluating retrieval quality through a dedicated evaluation harness, optimizing for end-to-end latency and accuracy across modular pipeline architectures.

Why does my RAG retrieval quality drop and how can I diagnose it?▼

Diagnose RAG retrieval quality issues by applying systematic phase-wise evaluation, checking chunking configurations and embedding models, and measuring performance against modular pipeline architectures.

Can I build a modular RAG architecture suitable for production deployment?▼

Yes, you can build a modular RAG architecture suitable for production deployment, supporting tiered configurations from Naive to Advanced designs that cover end-to-end system requirements.

How does an evaluation harness improve RAG pipeline accuracy and latency?▼

An evaluation harness improves RAG pipeline accuracy and latency by providing systematic measurement tools to test retrieval quality, embedding effectiveness, and end-to-end response generation performance.

When do I need an advanced tiered RAG architecture instead of a naive pipeline?▼

You need an advanced tiered RAG architecture instead of a naive pipeline when your production deployment requires modular components, systematic evaluation harnesses, and optimized end-to-end latency and accuracy.