Skill · AI & Development

RAG System Designer

Architect custom RAG pipelines based on your specific data and query patterns. Move beyond tutorial defaults to high-accuracy AI. Install in 30 seconds.

Category: AI & Development
Deliverable: 1 .skill bundle
Outputs: —
Last updated: 13 Jun 2026

$12.99 One-time · lifetime updates

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Overview

What RAG System Designer does.

RAG System Designer works through your corpus and query characteristics before recommending anything. It profiles document structure to determine the right chunking strategy, assesses query vocabulary to decide whether dense retrieval alone is sufficient or hybrid search is warranted, and evaluates your tolerance for wrong answers to set grounding strictness. It then produces a full pipeline specification covering chunking approach, embedding model selection, retrieval architecture, metadata filtering, reranking placement, vector store choice, and a golden question set for measuring retrieval quality over time.

A typical input: 'We have 4,000 internal engineering docs ranging from short Confluence pages to 60-page PDF specs. Queries are mix of precise lookups (find the error code for X) and exploratory (how does our auth system handle token expiry). We use Python and Postgres already, and wrong answers in production are a real problem.' That description is enough to start a calibrated design session.

The output is a structured architecture spec, not a list of generic tips. Example excerpt: Chunking — document-aware splitting at section headers with 200-token overlap at boundaries; dense retrieval insufficient given exact-term queries, recommend BM25 + dense hybrid with Reciprocal Rank Fusion; reranking layer not warranted at current corpus size, revisit above 20k chunks; golden set of 40 representative queries defined before first evaluation run; grounding: citation required for every factual claim, refusal on low-confidence retrievals.

Who it's for

Engineers and technical product managers building document QA, internal knowledge bases, or grounded AI assistants who have hit the limits of tutorial-based RAG setups and need retrieval that actually holds up against their specific corpus and production query patterns.

How it works

Three steps. About two minutes.

Install

Add the .skill file to your Claude app. ~10 seconds.

Run it on your work

Invoke the skill and paste in your material.

Apply the output

Review, keep what works, and use it.

In depth

Why a Claude skill beats a prompt template.

A copy-paste prompt runs one static pass and stops. A skill is a bundled program — instructions, examples, and a workflow Claude runs as a unit: it asks for the right input, applies the same pattern every time, and returns the structured outputs above.

FAQ

Common questions.

What do I need to provide to get a useful design?

Describe your corpus (document types, volume, structure), the kinds of queries users will ask, your existing tech stack, and how costly a wrong or hallucinated answer is. The more specific you are, the more calibrated the output. You can also say 'decide for me' and the skill will apply reasoned defaults and flag assumptions.

Does this skill write the actual retrieval code, or only design the architecture?

It produces an architecture specification and actionable decisions you can hand to a developer or use to configure your pipeline. It can include pseudocode or configuration sketches where that clarifies a recommendation, but its primary output is a design document, not a complete implementation.

What output formats does it return?

It adapts to your workflow: a structured spec document for a new build, a short decision list for a quick architecture question, or a checklist of changes if you are improving an existing pipeline. Specify which you need or it will default to a structured document for full design requests.

Will it tell me which vector database to use?

Yes, and it will give a reason tied to your specific constraints — not a generic comparison table. The recommendation factors in your existing stack, corpus size, whether you need metadata filtering, and operational complexity your team can realistically manage.

Does this skill cover evaluation, or only the retrieval pipeline?

Evaluation is part of the design, not an afterthought. The skill specifies a golden question set construction process and the retrieval metrics (precision at k, recall, answer faithfulness) appropriate for your quality bar, because a pipeline without defined metrics cannot be reliably improved.