super-ai-ml-foundation

name	super-ai-ml-foundation
description	AI/ML foundations: model selection, prompt design, RAG, embeddings, and vector search. Use for core AI app design and build.

Super AI/ML Foundation

Overview

Establish the core AI/ML architecture before building agents or ops layers.

User Intent Examples

• "Need help with AI Engineering for my product/site."
• "Create a plan for Prompt Engineering."
• "Audit or improve RAG Engineering."

Workflow

1. Confirm use case, success criteria, latency, and cost targets.
2. Select model family and deployment approach (hosted vs self-hosted).
3. Design prompts, tool interfaces, and guardrails.
4. Plan and implement RAG: chunking, embeddings, indexing, retrieval.
5. Validate relevance, quality, and failure modes with small tests.
6. Document tradeoffs, risks, and next experiments.

Minimal Intake Questions

• Primary goal or outcome
• Scope (pages, systems, teams, or timeframe)
• Constraints (tools, budget, timeline)

Output Format

• Use-case brief and success metrics
• Model choice with rationale
• Prompt and tool plan
• RAG architecture plan
• Risks, mitigations, and next steps

Routing Map (Modules)

• AI Engineering -> references/modules/ai-engineer.md
• Prompt Engineering -> references/modules/prompt-engineering-patterns.md
• RAG Engineering -> references/modules/rag-engineer.md

Bundled References

• references/modules/
• scripts/
• assets/
• agents/

Compatibility Notes

• If any module references slash commands or tool-specific paths, translate them into plain-language steps.
• Keep outputs platform-agnostic unless the user specifies a specific tool, stack, or agent.

Guardrails

• Do not claim benchmark results without data.
• Separate measured results from hypotheses.
• Prefer the smallest viable model and simplest retrieval setup.