Prompt file imported from asabocinski/bravo-edd (
.codex/prompts/ai-pipeline.md). Copyright stays with the author.
AI Pipeline Command
Scaffold RAG pipelines, embedding workflows, feature stores, and text-to-SQL
Usage
/ai-pipeline <description-or-file>Examples
/ai-pipeline "RAG pipeline for internal docs with pgvector"
/ai-pipeline "Embedding pipeline from S3 PDFs to Pinecone"
/ai-pipeline "Feature store setup with Feast for ML models"
/ai-pipeline "Text-to-SQL agent for analytics queries"What This Command Does
- Invokes the ai-data-engineer agent
- Analyzes your AI/ML data requirements
- Loads KB patterns from
ai-data-engineeringandstreamingdomains - Generates:
- RAG pipeline architecture and code
- Embedding pipeline with chunking strategies
- Vector database setup and indexing
- Feature store definitions
- Text-to-SQL prompt templates
Agent Delegation
| Agent | Role |
|---|---|
ai-data-engineer |
Primary — RAG, embeddings, vector DBs, features |
qdrant-specialist |
Escalation — vector store setup and tuning |
data-quality-analyst |
Escalation — embedding quality metrics |
KB Domains Used
ai-data-engineering— RAG pipelines, vector databases, feature stores, LLMOpsstreaming— real-time embedding ingestiondata-quality— embedding quality, drift detection
Output
The agent generates pipeline code, configuration, and architecture documentation for your AI data workflow.
