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":["claude-code","codex"]}

RAG Patterns — Retrieval-Augmented Generation

Operating contract

Inputs

Input	Required	Purpose
Domain evidence	yes	question set, authoritative corpus, tenancy and access rules, freshness target, citation needs, and evaluation set

Outputs

• Produce: retrieval architecture, ingestion/chunking policy, index and filter contract, grounded-answer schema, and evaluation report.

Capability and permission boundaries

Default to read-only analysis. Read only scoped records; redact secrets and regulated data. Writes, execution, network calls, production configuration, customer communication, billing changes, and delegation require explicit authority and an identified owner. Never widen tenant, time-window, or system scope implicitly.

Degraded mode

When required telemetry, evidence, execution, network access, or write authority is unavailable, return a partial result with each unassessed item labelled, preserve the safest existing state, and state the evidence or approval needed to continue. Never convert missing evidence into a pass.

Decision rules

Condition	Action
Scope, owner, or threshold is missing	Stop the affected decision and request it
Evidence is incomplete but read-only analysis is safe	Produce a qualified partial result and gap list
A mutation exceeds authority or tenant boundary	Block it and route for approval
Evidence meets the stated threshold	Issue the output with provenance and owner

Anti-Patterns

• Treating absent evidence as success. Fix: mark the check unassessed and name the missing source.
• Expanding one tenant or workflow to all tenants. Fix: enforce supplied scope at every query and action.
• Performing a production write during analysis. Fix: emit a reviewed change plan until authority is explicit.
• Reporting a metric without population, window, or source. Fix: attach all three.
• Hiding a failed threshold inside an average. Fix: report failure slices and the remediation owner.

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...

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.