RAG Architect

Name: RAG Architect
Brand: AgentVolt
Price: 49.00 USD
Availability: InStock

Designs and implements production-grade RAG systems by chunking documents, generating embeddings, configuring vector stores, building hybrid search pipelines.

Development / Data & MLdevelopmentdata-ml

by AgentVoltv1.0.0Published 1y ago1 install

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Before & after

Same prompt, same agent — the only difference is the skill.

Prompt“Design a RAG system for my product documentation Q&A”

Beforegeneric AI agent

I can help you build a production RAG system from scratch. First, you'll need to chunk your documents into smaller pieces, generate embeddings for those chunks, and set up a vector database to store them. Then you'd build a search pipeline that retrieves relevant document chunks when a user asks a question. There are several different vector database options available to choose from. What technology stack are you currently using in your infrastructure? Do you want to include hybrid search combining keyword and semantic matching?

Afterwith RAG Architect

PRODUCTION RAG ARCHITECTURE SPECIFICATION:
CHUNKING STRATEGY: Recursive text splitter with 512 token chunks as default size, 100 token overlap between chunks. Preservation rule: never split within code examples, tables, or images. Include metadata tags: page_title, section_name, update_date, doc_version, author. Chunk quality validation.
EMBEDDINGS MODEL: OpenAI text-embedding-3-small (1536 vector dimensions). Cost approximately 0.02 USD per 1 million tokens. Regenerate full embedding index monthly to capture new docs and updates. Batch processing runs every Sunday 2 AM UTC automatically.
VECTOR STORE INFRASTRUCTURE: Postgres database with pgvector extension installed. Self-hosted deployment, no third-party vendor dependency. Index type: HNSW (Hierarchical Navigable Small World) for 99th-percentile recall accuracy. Automatic reindex trigger after 10,000 new embeddings accumulated.
HYBRID SEARCH LOGIC: BM25 keyword matching combined with vector semantic similarity. Fusion weighting: 60% semantic similarity score, 40% keyword matching score. Reranker: Cohere rerank-3-english model, re-scores top 20 results before returning top 5 to end user.
RETRIEVAL PIPELINE FLOW: User query input → Query rewriting for clarity (LLM, under 1 second latency) → Hybrid search execution (retrieve 20 candidates) → Rerank candidates → LLM answer generator → Response delivery to user.
EVALUATION & MONITORING: Quarterly RAGAS benchmark evaluation on 100 held-out queries. Track precision (target 0.85), recall (target 0.80), F1 score (target 0.75). Adjust chunk size and overlap parameters if metrics drift below targets.

About this skill

name: rag-architect description: Use when Designs and implements production-grade RAG systems by chunking documents, generating embeddings, configuring vector stores, building hybrid search pipelines, applying reranking, and evaluating ret...

Rag Architect

Designs and implements production-grade RAG systems by chunking documents, generating embeddings, configuring vector stores, building hybrid search pipelines, applying reranking, and evaluating retrieval quality. Use when building RAG systems, vector databases, or knowledge-grounded AI applications requiring semantic search, document retrieval, context augmentation, similarity search, or embedding-based indexing.

What you get

Public GitHub repo
the skills/rag-architect folder with SKILL.md and references.

Customize your output

Fork the repo and extend the skill's reference files for your own stack conventions.

Example output

Activates on a matching request (e.g. building or reviewing Rag Architect code) and can chain with other skills in the pack.

Best for

Full-stack developers and engineering teams using Claude Code.

SKILL.md preview

SKILL.md

---
name: rag-architect
description: Use when designing or implementing a production-grade RAG system — chunking documents, generating embeddings, configuring a vector store, building hybrid search, applying reranking, or evaluating retrieval quality.
version: 1.0.0
category: Development / Data & ML
author: AgentVolt
license: proprietary
tags:
  - development
  - data-ml
---

# Rag Architect

Designs and implements production-grade RAG systems by chunking documents, generating embeddings, configuring vector stores, building hybrid search pipelines, applying reranking, and evaluating retrieval quality.

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