peterbamuhigire/ai-rag-patterns

RAG Patterns — Retrieval-Augmented Generation

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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.
• 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.

References

• Use the links and companion skills already referenced in this file when deeper context is needed.

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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

Extended guidance for ai-rag-patterns was moved to references/skill-deep-dive.md to keep this entrypoint compact and fast to load.

Use that deep dive for:

• 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
• Additional deep-dive sections continue in the reference file.