AI Learning Path: Beginner to Advanced

This category is designed to be read as a progression, not as a random archive. The goal is to help you move from core AI product concepts to production-grade LLMOps decisions in a clear order.

Beginner: understand the product shape first

Start here if you are new to shipping AI features and want a mental model before going deep into operations.

1. OpenAI Responses API Agent Architecture
2. Prompt Engineering Operations
3. AI Agent Guardrails

At this stage, focus on:

• how AI features connect to user workflows
• where prompts, tools, and policies sit in the stack
• why safety boundaries matter before scale

Intermediate: learn how quality and workflow control work

Move here once you understand the basic runtime shape and want to operate AI features more reliably.

1. RAG Evaluation Playbook
2. AI Evaluation Rubric for Production Teams
3. Model Spec Product Governance Playbook

At this stage, focus on:

• evaluation criteria and failure classes
• grounding and trustworthiness
• policy and governance decisions around product behavior

Advanced: operate AI as a production platform

Read these when you are thinking about scale, cost, and platform-wide operating models.

1. LLMOps Platform Architecture
2. LLM Cost Guardrails and AI FinOps

At this stage, focus on:

• platform boundaries
• model routing and cost budgets
• operational controls that keep AI sustainable over time

What you should be able to do after this path

• explain the difference between prompt tuning and product architecture
• design evaluation gates before changing models or prompts
• define safety, cost, and quality controls for production AI systems

If you are unsure where to begin, start with the beginner section and only move forward once the runtime flow feels concrete.

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