ai-rag-patterns

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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...

AI & Automation 25 stars 6 forks Updated 3 days ago MIT

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Skill Content

# 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 ...

Details

Author
peterbamuhigire
Repository
peterbamuhigire/chwezi-dev-engine
Created
7 months ago
Last Updated
3 days ago
Language
HTML
License
MIT

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