Name: RAG
Availability: InStock
Author: ivangdavila

AI Skill Market Insights

Real data. Real impact.

Popularity

Rising

Emerging

Active Users

0+

Developers

Time Saved

2+ hrs

Per week

Source

GitHub

Open source

Be Part of the 0+ Developer Community

Skills give you superpowers. Install in 30 seconds.

When to Use

User wants to implement, improve, or troubleshoot Retrieval-Augmented Generation systems.

Quick Reference

Topic	File
Pipeline components & architecture	`architecture.md`
Implementation patterns & code	`implementation.md`
Evaluation metrics & debugging	`evaluation.md`
Security & compliance	`security.md`

Core Capabilities

Architecture design — Select embedding models, vector DBs, and chunking strategies based on requirements
Implementation — Write ingestion pipelines, query handlers, and update logic
Retrieval optimization — Tune top-k, reranking, hybrid search parameters
Evaluation — Build test datasets, measure recall/precision, diagnose failures
Production ops — Monitor quality drift, set up alerts, debug degradation
Security — PII detection, access control, compliance requirements

Decision Checklist

Before recommending architecture, ask:

What document types and volume?
Latency requirements (real-time chat vs batch)?
Update frequency (how often do docs change)?
Access control needs (who can see what)?
Compliance constraints (GDPR, HIPAA, SOC2)?
Budget (managed vs self-hosted, embedding costs)?

Critical Rules

Never skip access control — Filter at retrieval time, not after
Always overlap chunks — 10-20% prevents context loss at boundaries
Evaluate before optimizing — Build eval dataset first, then tune
Same embedding model — Query and documents must use identical model
Monitor similarity scores — Dropping averages signal drift or issues
Plan for deletion — GDPR erasure requires re-embedding capability

Common Failure Patterns

Symptom	Likely Cause	Fix
Wrong docs retrieved	Query too vague, poor chunks	Query expansion, smaller chunks
Relevant doc missed	Not indexed, low similarity	Check ingestion, hybrid search
Hallucinated answers	Context too short	Increase top-k, better reranking
Slow responses	Large chunks, no caching	Optimize chunk size, cache embeddings
Inconsistent results	Non-deterministic reranking	Set seeds, use stable sorting

RAG

AI Skill Market Insights

Be Part of the 0+ Developer Community

When to Use

Quick Reference

Core Capabilities

Decision Checklist

Critical Rules

Common Failure Patterns

Quick Start

Manual Installation

TEAR & SHARE

Tags

Multi-Agent Patterns

Memory Systems Design

Context Degradation Detection

Context Optimization

Tool Design for Agents

Channels

Learn

Compare

Company