Deterministic AI Workflows learning roadmap.
Separate language understanding from business control to build reliable, auditable, and testable AI-assisted workflows. Work through 5 modules, then use the connected questions to check what you can explain without the notes.
- STEP 1
Foundations & responsibility boundaries
- Overview · 2 min
- Why Not Let the LLM Control the Entire Workflow? · 2 min
- The Architecture at a Glance · 2 min
- What the LLM Does · 2 min
Checkpoint: explain the ideas above with an example and a limitation before continuing.
- STEP 2
State, rules & graph execution
- What the Deterministic Rules Engine Does · 2 min
- Workflow State and the Ledger · 2 min
- Graphs, Nodes, and Transitions · 2 min
Checkpoint: explain the ideas above with an example and a limitation before continuing.
- STEP 3
Scope, safety & external actions
- Out-of-Scope Intent Handling · 2 min
- Guardrails and Authorization · 2 min
- Connectors and External Actions · 2 min
Checkpoint: explain the ideas above with an example and a limitation before continuing.
- STEP 4
Testing, reuse & architecture choices
- Why Scripted Testing Matters · 2 min
- Reusable Engine vs. Application · 2 min
- LLM-Driven vs. Deterministic Workflow · 2 min
Checkpoint: explain the ideas above with an example and a limitation before continuing.
- STEP 5
Production lessons & implementation
- Challenges and Practical Lessons · 2 min
- A Practical Implementation Checklist · 2 min
- Key Takeaways · 2 min
Checkpoint: explain the ideas above with an example and a limitation before continuing.
Check your understanding
After the lessons, rehearse the 16 connected questions. Revisit the lesson when you cannot yet explain why the answer works.
Practice Deterministic AI Workflows Review the interview guide