Comprehensive field guide
AI Products Field Guide
AI Products Field Guide organizes the decisions that matter for founders and organizations building AI-powered SaaS, support, research, document, recommendation, classification, media, or agent tools: the current workflow, ownership, implementation choices, rollout risk, and acceptance evidence.
Working artifact
AI Products integration-boundary map
Use the boundary map to show what crosses systems, where it can fail, and how the result will be reconciled.
| Boundary | Information moving | Failure to test |
|---|---|---|
| Hosted and open-source model integration | AI product discovery and feasibility | A model demo has no dependable product workflow |
| RAG, fine-tuning, and tool use | Model and provider selection | The product uses AI where ordinary software is better |
| AI-centered interface design | Application UX and human review | Quality cannot be evaluated before release |
Read the situation before naming the solution
A model demo has no dependable product workflow. Confirm who encounters it, where it occurs, and what changed before it appeared. Then distinguish the visible symptom from dependencies such as hosted and open-source model integration.
- A model demo has no dependable product workflow
- The product uses AI where ordinary software is better
- Quality cannot be evaluated before release
Protect the current state
For AI Product & Application Development, confirm account ownership, current exports or backups, recovery options, and recent changes before touching production. Preserve exact errors and timestamps that may disappear after a restart or update.
- Access owner
- Current backup
- Restore method
- Change history
Define the smallest useful result
Frame the first scope around AI product discovery and feasibility and one observable acceptance journey. Treat model and provider selection as a later phase unless the evidence shows it is a true dependency.
- AI product discovery and feasibility
- Model and provider selection
- Application UX and human review
Compare repair, extension, and replacement
Repair fits when the core remains sound. Extension fits when the boundary around hosted and open-source model integration is understood. Replacement fits when ownership, architecture, or operating risk prevents a responsible change.
- Time to value
- Data risk
- Reversibility
- Maintenance ownership
Plan implementation and launch
Sequence work around RAG, fine-tuning, and tool use. Protect the people affected by “A model demo has no dependable product workflow,” and define the point where rollback is safer than continuing.
- RAG, fine-tuning, and tool use
- AI-centered interface design
- Evaluation and observability systems
Verify and hand off
Repeat the original journey, test a nearby failure, and document the result. A successful handoff leaves founders and organizations building AI-powered SaaS, support, research, document, recommendation, classification, media, or agent tools able to understand what changed, who owns it, and what happens next.
- Acceptance evidence
- Current documentation
- Monitoring owner
- Prioritized next step