What does the Kai skill do?

Build production RAG: chunking, hybrid search, reranking, and an eval harness that proves recall improved. Load it once into Claude Projects and you get a configured RAG / Retrieval Architect without re-explaining context at the start of every session. Build and evaluate against a held-out test set in a development or staging environment, add guardrails, cost and rate limits, and human review, and roll out behind evals and monitoring before serving agents or LLM features in production.

How do I install this skill file?

Download the .skill package (it contains SKILL.md), paste the contents into Claude Projects Instructions or your AI's system prompt, add your own context and start your first session. Works with Claude, ChatGPT, or any AI chat that accepts system prompts.

Which AI tools does this skill work with?

Works with Claude (recommended), ChatGPT, Gemini, Perplexity and Copilot, and any AI chat that accepts system prompts. Claude Projects gives the best results.

What is included in this download?

One .skill package delivered instantly after purchase: the full SKILL.md role configuration plus a worked-example file with a real scenario so you see the quality before you rely on it. No subscription, yours permanently.

How is this different from using Claude without the Kai skill?

Without a skill file your AI starts every session as a general assistant. With Kai loaded it applies RAG / Retrieval Architect methodology from the first message, with consistent quality every time. Build and evaluate against a held-out test set in a development or staging environment, add guardrails, cost and rate limits, and human review, and roll out behind evals and monitoring before serving agents or LLM features in production.

RAG / Retrieval Architect AI Skill for Claude & ChatGPT

Name: Kai — RAG / Retrieval Architect AI Skill
Brand: KissMySkills
Availability: InStock

Kai — RAG / Retrieval Architect AI Skill

$34.00

Kai — RAG / Retrieval Architect AI Skill

$34.00

⚡ Instant download ◆ Claude & ChatGPT ∞ Keep forever

Instant download · 30-day money-back guarantee. Pay once, keep forever — no subscription. Refund policy

Build production RAG: chunking, hybrid search, reranking, and an eval harness that proves recall improved.

Chunking strategy and metadata design
Hybrid search (BM25 + dense) with RRF and cross-encoder reranking
Query transformation: multi-query, HyDE, decomposition
Retrieval eval: recall@k, MRR, nDCG, faithfulness, with before/after

AI engineers whose RAG hallucinates or misses, who want retrieval tuned by numbers, not vibes.A senior RAG engineer bills $130+/hr, this is one file, yours forever.

// what's inside

Drop Kai into Claude and get a senior RAG architect who builds retrieval that returns the right context, measured with an eval harness before you tune.

Kai treats retrieval as an information-retrieval problem first: corpus prep and chunking strategy, embedding selection, vector index tuning (HNSW/IVF), hybrid search (BM25 + dense) with RRF fusion, cross-encoder reranking, query transformation (multi-query, HyDE, decomposition), retrieval and end-to-end evaluation (recall@k, MRR, nDCG, faithfulness), grounding and citation, freshness, and the cost and latency of every hop. Measure before you tune. Always evaluate on a held-out labeled set in dev/staging before serving in production.

What you get

→Chunking strategy and metadata design
→Hybrid search (BM25 + dense) with RRF and cross-encoder reranking
→Query transformation: multi-query, HyDE, decomposition
→Retrieval eval: recall@k, MRR, nDCG, faithfulness, with before/after

📄 kai-rag-retrieval-architect.skill Under 2 min install Works with Claude, ChatGPT & any AI chat

How to install

Download the .skill package → open Claude → paste SKILL.md into your Project Instructions or system prompt → describe your requirement → Kai builds the answer. Includes a full worked example so you see exactly what you get.

kai-rag-retrieval-architect.skill

# Kai - RAG / Retrieval Architect

You are Kai, a senior RAG / Retrieval Architect. You build retrieval that returns the right context and you measure it before you tune.

## How you work
1. Understand the corpus and the real queries
2. Build a labeled eval set first
3. Chunk, embed, index; retrieve hybrid, fuse (RRF), rerank
4. Measure (recall@k, MRR, nDCG, faithfulness), diagnose, fix, report before/after

Always evaluate retrieval on a held-out labeled set in dev/staging, and review citations and faithfulness before serving in production.

Excerpt from the actual file you'll download.

// try it

prompt

$My RAG hallucinates config values and misses ticket-specific answers. Diagnose it and give me the fix with numbers.

Kai returns the eval set, the chunking decision with rationale, hybrid-retrieval + RRF + rerank code, a metrics table (recall@5, MRR, nDCG, faithfulness) and a diagnosed failure fixed with before/after numbers, grounded and cited.

// how to install Under 2 minutes

Four steps. Any AI chat.

01
Download the file
After checkout, the download link lands in your inbox. Save the file anywhere on your device.
02
Open your AI chat
Claude, ChatGPT, Gemini, Grok, or Copilot — whichever one you already use.
03
Paste the file contents
Drop it into the system prompt, Project instructions, or custom instructions field.
04
Start working
Your AI is now configured as a specialist. Ask it anything inside its domain.