enrich

MANDATORY PREPARATION

Invoke /agent-workflow — it contains workflow principles, anti-patterns, and the Context Gathering Protocol. Follow the protocol before proceeding — if no workflow context exists yet, you MUST run /teach-maestro first. Consult the knowledge-systems reference in the agent-workflow skill for RAG architecture, chunking strategies, and retrieval patterns.

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Add knowledge sources to ground the workflow in facts. Without grounding, agents hallucinate. With grounding, they cite sources.

Knowledge Source Assessment

Identify what knowledge the workflow needs:

Knowledge Type	Source	Update Frequency	Access Pattern
Domain docs	Internal docs, specs	Monthly	Semantic search
Code context	Codebase	Real-time	Code search
User data	Database, CRM	Real-time	Structured query
External data	APIs, web	Real-time	API call
Historical	Logs, past interactions	Daily	Time-range query

Add RAG Pipeline

For document-based knowledge (consult the knowledge-systems reference in the agent-workflow skill):

1. Select documents: Identify the authoritative source documents
2. Chunk strategy: Choose chunking based on document type (semantic > token-based)
3. Embed: Use appropriate embedding model for the domain
4. Index: Store in vector database with metadata
5. Retrieve: Implement hybrid search (semantic + keyword)
6. Inject: Add retrieved context to the prompt with source attribution

Add Structured Data

For database-backed knowledge:

1. Define the query interface: Natural language → structured query
2. Add guardrails: Read-only access, query complexity limits
3. Format results: Transform raw data into context the model can use
4. Attribute: Include data source and freshness in the context

Add Real-Time Data

For live information:

1. Identify APIs: What external services provide the needed data
2. Cache strategy: How often does the data change? Cache accordingly
3. Fallback: What happens when the API is down?
4. Attribution: Include data timestamp and source

Enrichment Checklist

• Every knowledge source has attribution (source, date, confidence)
• Retrieval quality tested independently of generation quality
• Chunk sizes tested and optimized for the document types
• Fallbacks exist for all external knowledge sources
• Knowledge base has a refresh/update strategy
• PII is handled appropriately in knowledge sources

Recommended Next Step

After enrichment, run /evaluate to test retrieval quality, or /iterate to set up continuous monitoring of knowledge freshness.

NEVER:

• Index everything without curation (garbage in = garbage out)
• Skip source attribution (hallucination without attribution is undetectable)
• Build RAG without testing retrieval quality first
• Use fixed chunk sizes for all document types
• Assume embedding similarity equals relevance