💭 PromptOps Policy Coach — From Metrics to Mechanisms You Can Trust

If you’ve ever tried to scale AI inside a big company, you already know the truth: models aren’t the hard part — trust is.

And trust doesn’t show up because we ask for it; it shows up because we can measure what’s happening and govern how it behaves.

Last week I shared Why Metrics Matter — how velocity, predictability, and flow efficiency quietly fixed delivery pain on our AI teams.

Today I’m taking that one step further with PromptOps Policy Coach — a platform that turns those same delivery insights into governable AI systems.

💡 This article is part of my Weekend AI Project Series, where I turn weekend build ideas into production-ready AI systems.

👉 Read Part 1 — Adventures in Vibe Coding

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🎯 TL;DR

• 🧠 What it is: A policy Q&A system that runs one question through five prompt frameworks and tracks cost/performance in real time.
• 💡 Why it exists: To make AI consistent, explainable, and affordable across HR/Legal/IT.
• ⚙️ What it proves: Enterprise AI isn’t “just prompts.” It’s patterns + governance + metrics working together.

Frameworks: CRAFT, CRISPE, Chain-of-Thought, Constitutional AI, ReAct

RAG: Custom numpy-based retrieval

Cost: < $0.01/query (OpenAI GPT-4o-mini)

Deploy: Docker or Google Cloud Shell

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💬 The backstory

In big orgs, three things kill AI rollouts:

1. Inconsistent outputs — same question, five answers.
2. Runaway costs — invisible API usage that eats budgets alive.
3. Slow adoption — heavy infra that scares off internal teams.

So I standardized how the AI reasons, made sources explicit with RAG, and surfaced cost & performance as first-class citizens. That became PromptOps.

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⚙️ A quick tour

1) One clean interface

Ask a policy question. Pick a framework. Get the answer and see what it cost. Simple and transparent.

2) Five brains, one question

• ReAct — think → act → observe
• CRISPE — helpful, human tone
• CRAFT — exec-level structure
• Chain-of-Thought — careful reasoning
• Constitutional AI — principles + self-checks

3) RAG that’s boring on purpose

Custom, numpy-based, dependency-light. Fast and deployable anywhere.

4) Live metrics

Tokens, cost, response time — per framework — because if you can’t see it, you can’t trust it.

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🏗️ Architecture

graph TB
    A[Company Docs] --> B[Chunk & Index]
    B --> C[Vector Search (numpy + embeddings)]
    E[User Query] --> D[Multi-Framework Engine]
    C --> D
    D --> F[CRAFT]
    D --> G[CRISPE]
    D --> H[Chain-of-Thought]
    D --> I[Constitutional AI]
    D --> J[ReAct]
    F --> K[OpenAI GPT-4o-mini]
    G --> K
    H --> K
    I --> K
    J --> K
    K --> L[Response + Sources + Metrics]
    L --> M[Cost Tracking]
    L --> O[Dashboard]

🧩 Engineering highlights

✅ Custom RAG > heavy stacks — smaller image, fewer headaches, clearer control.

✅ Cloud Shell optimized — consistent demo environment (no local setup drama).

✅ OpenAI v1 client — cheaper, future-proof.

✅ Defensive code — zero-error demos.

Benchmarks: 2.4–8.4s response | $0.0001–$0.0002/query | <200MB footprint.

🚀 What it means for enterprise teams

HR/IT/Legal → consistent answers with source links

Finance → predictable usage and spend

Compliance → logs and auditability

Product → compare frameworks and ship what users prefer

It’s a working prototype of how AI governance should feel — transparent, fast, dependable.

🛠️ Quick start

Cloud Shell / Local

git clone https://github.com/marcusmayo/machine-learning-portfolio.git
cd machine-learning-portfolio/prompt-ops-policy-coach
pip install -r requirements.txt
streamlit run app/enhanced_app.py \
  --server.port 8501 --server.address 0.0.0.0 \
  --browser.serverAddress localhost \
  --browser.gatherUsageStats false \
  --server.enableCORS false --server.enableXsrfProtection false

Docker

docker build -t policy-coach .
docker run -d -p 8080:8080 --name policy-coach-prod --env-file .env policy-coach

🧭 What’s next

Framework marketplace · SSO/RBAC · QA suite for prompt consistency · Cost optimizer · Kubernetes scaling.

💬 Final thought

If Why Metrics Matter was about measuring, PromptOps is about governing.
Measure → improve. Govern → trust.

🧠 Read My AI Build Logs (CTA)
Medium → https://medium.com/@mayo.marcus
Dev.to → https://dev.to/marcusmayo

📇 Connect
LinkedIn → https://lnkd.in/e9CBVihC
X / Twitter → https://x.com/MarcusMayoAI
Email → [e-mail]

💻 Portfolio
Part 1 — https://github.com/marcusmayo/machine-learning-portfolio
Part 2 — https://github.com/marcusmayo/ai-ml-portfolio-2