ai-rag-patterns

name	ai-rag-patterns
description	Use when building features that answer questions from private data, documents, policies, or time-sensitive information — RAG architecture, chunking strategies, hybrid search, re-ranking, vector databases, evaluation, agentic RAG, multimodal RAG...
metadata	{"portable":true,"compatible_with":["Codex","codex"]}

RAG Patterns — Retrieval-Augmented Generation

Acknowledgement: Shared by Peter Bamuhigire, techguypeter.com, [phone].

Use When

• Use when building features that answer questions from private data, documents, policies, or time-sensitive information — RAG architecture, chunking strategies, hybrid search, re-ranking, vector databases, evaluation, agentic RAG, multimodal RAG...
• The task needs reusable judgment, domain constraints, or a proven workflow rather than ad hoc advice.

Do Not Use When

• The task is unrelated to ai-rag-patterns or would be better handled by a more specific companion skill.
• The request only needs a trivial answer and none of this skill's constraints or references materially help.

Required Inputs

• Gather relevant project context, constraints, and the concrete problem to solve; load references only as needed.
• Confirm the desired deliverable: design, code, review, migration plan, audit, or documentation.

Workflow

• Read this SKILL.md first, then load only the referenced deep-dive files that are necessary for the task.
• Apply the ordered guidance, checklists, and decision rules in this skill instead of cherry-picking isolated snippets.
• Produce the deliverable with assumptions, risks, and follow-up work made explicit when they matter.

Quality Standards

• Keep outputs execution-oriented, concise, and aligned with the repository's baseline engineering standards.
• Preserve compatibility with existing project conventions unless the skill explicitly requires a stronger standard.
• Prefer deterministic, reviewable steps over vague advice or tool-specific magic.

Anti-Patterns

• Treating examples as copy-paste truth without checking fit, constraints, or failure modes.
• Loading every reference file by default instead of using progressive disclosure.

Outputs

• A concrete result that fits the task: implementation guidance, review findings, architecture decisions, templates, or generated artifacts.
• Clear assumptions, tradeoffs, or unresolved gaps when the task cannot be completed from available context alone.
• References used, companion skills, or follow-up actions when they materially improve execution.

Evidence Produced

Category	Artifact	Format	Example
Correctness	RAG retrieval evaluation report	Markdown doc covering recall / precision / answer-quality on a fixed eval set	docs/ai/rag-eval-2026-04-16.md
Data safety	Index ingestion + tenancy isolation note	Markdown doc covering chunking, source filtering, and per-tenant index segregation	docs/ai/rag-tenancy-note.md

References

• Use the references/ directory for deep detail after reading the core workflow below.

Overview

RAG solves the core LLM limitation: they only know what they were trained on. Use RAG to inject private data (invoices, menus, policies, reports) into every AI response.

Core principle: RAG = look up a database + LLM synthesises the results. The LLM never needs to "know" your data.

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When to Use RAG

Condition	Action
Knowledge base < 200K tokens (~500 pages)	Include everything in context — no RAG needed
Knowledge base > 200K tokens	Use RAG
Data changes frequently (menus, prices, stock)	RAG (update documents, not model)
Data is private/confidential	RAG (keeps data out of training pipelines)
Need source citations	RAG (chunks are traceable to source)
Model needs brand voice / domain jargon	Fine-tune instead

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RAG vs Fine-Tuning

Factor	RAG	Fine-Tuning
Up-to-date content	✅ Yes (add docs anytime)	❌ Stale until retrained
Hallucinations	✅ Lower (document-grounded)	❌ Higher
Source citations	✅ Yes	❌ No
Brand voice control	❌ Weak	✅ Strong
Domain jargon	❌ Weak	✅ Strong
Up-front cost	✅ Lower	❌ High

Default: start with RAG. Fine-tune only when RAG + prompt engineering cannot deliver the required tone or vocabulary.

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Additional Guidance

Guidance is split across two reference files so this entrypoint stays compact.

references/skill-deep-dive.md — architecture, chunking, retrieval, schema:

• Pipeline Architecture
• Chunking Strategies
• Embedding Model Selection
• Vector Database Selection
• Retrieval Algorithms
• Re-Ranking
• Full RAG Query Algorithm
• Query Rewriting (Multi-Turn)
• RAG Schema (Multi-Tenant)
• Evaluation Framework
• Production Patterns
• Agentic RAG
• Multimodal RAG, Edge Cases, Cost Optimisation, Sources

references/production-rag.md — the progression from draft to production and the gates before shipping:

• RAG Maturity Model — Naive → Advanced → Modular
• Query Transformation — HyDE, Multi-Query, Step-Back
• Contextual Compression
• Self-RAG
• RAGAS Evaluation — 4 metrics with production thresholds
• Embedding Pipeline — batching, upserts, re-embed triggers, $/1M-token table
• Cost Management Decision Tree — concrete dollar figures per branch
• Failure Mode Playbook — empty, irrelevant, hallucinated, stale
• Gates Before Shipping

Load the production file when building a RAG system that has to pass evaluation gates, survive multi-tenant review, or hit a cost budget under load.

Multi-Tenant Addendum

This skill describes RAG patterns in general. When the RAG feature ships inside a multi-tenant SaaS, the production answer is ai-rag-multi-tenant — per-tenant ingestion pipelines, vector store partitioning, tier-specific chunking and embedding models, defence-in-depth retrieval security, and citation grounding tied to live sources.

Cross-references:

• ai-rag-multi-tenant — multi-tenant RAG end-to-end.
• ai-tenant-isolation-patterns — vector-store partitioning tradeoffs and data-bleed tests.
• ai-on-saas-architecture — KB service as a control-plane service.
• ai-hallucination-slo-and-grounding — citation grounding + faithfulness SLO.
• ai-model-gateway — gateway-mediated retrieval calls.
• saas-tenant-data-portability-and-erasure — KB erasure cascade for embeddings.

Consolidated Child References

• Load references/routing.md to map retired AI child skill slugs to their reference modules.