Courses & Learning

This page is a working learning path, not an under-construction course page. You can use the modules below today with the free articles and tools already published on Vector Insight Global. A paid course may be added later, but the material here stands on its own.

Learning path: build a safer AI workflow

Module 1 — Understand agentic AI

Begin with What Is Agentic AI?. Focus on the difference between a model generating an answer and a workflow that can use tools, retain state or take actions.

Checkpoint: Write down one workflow you want to improve and identify its trigger, input and expected output.

Module 2 — Decide whether the workflow is ready

Complete the free AI Workflow Readiness Scorecard. Do not choose an automation platform yet. First identify gaps in process clarity, data, permissions, reliability and measurement.

Checkpoint: If your score is below 24/40, redesign the process before increasing automation.

Module 3 — Design permissions and human control

Use the AI Workflow Checklist to define minimum data access, actions that require approval, safe failure states, server-side credential handling and logging.

Checkpoint: List every action the workflow could take and mark each one as read-only, reversible write, high-impact write or prohibited.

Module 4 — Understand context and model limits

Read LLM Context Windows Explained and Why LLMs Hallucinate. The goal is to understand why adding more text is not the same as adding reliable knowledge.

Checkpoint: Identify which facts must come from a trusted external source rather than model memory.

Module 5 — Choose between prompting, retrieval and model adaptation

Use RAG vs Fine-Tuning vs Prompt Engineering as a decision guide. Match the method to the problem instead of choosing the most complex option by default.

Checkpoint: Explain in one sentence why your workflow needs prompting only, retrieval, fine-tuning or a combination.

Module 6 — Connect tools carefully

Read MCP in 2026: Practical Guide to understand how tool and data connections change the security boundary of an AI system.

Checkpoint: Reduce the workflow to the smallest set of tools and permissions required to complete the task.

Module 7 — Evaluate outputs

Use How to Evaluate AI Answers to create representative test cases and acceptance criteria.

Checkpoint: Build at least five normal cases, two ambiguous cases and two failure cases before relying on the workflow.

Module 8 — Pilot and measure

Run the workflow on a narrow scope. Measure time saved, correction rate, completion rate and the actual business or publishing outcome. Increase autonomy only after the evidence supports it.

Checkpoint: Define the condition that would make you roll the workflow back.

Optional deeper resource

The AI Agent Workflow Playbook — 2026 Edition is a paid 44-page digital guide that expands on workflow patterns, guardrails, evaluation and rollout planning. It is optional; the learning path above is available without a purchase.

How this learning path is maintained

We revise links and modules as the underlying guides change. Our research and review methodology explains how we source claims, use AI assistance, distinguish research from hands-on testing and retire overlapping pages.