Comprehensive field guide
The AI Products Field Guide
This field guide brings the main aI Product & Application Development decisions into one place: how to read the symptoms, protect the current state, choose a scope, compare paths, and verify an outcome that matters to founders and organizations building ai-powered saas, support, research, document, recommendation, classification, media, or agent tools.
Read the situation before naming the solution
Begin with a model demo has no dependable product workflow, who experiences it, and what changed before it appeared. Distinguish the visible symptom from the system boundary that may involve 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
Confirm ownership, access, backups, and a recovery path before changing production. Preserve exact errors and timestamps because they 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. Keep application UX and human review as a later phase unless 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 is understood; replacement fits when ownership, architecture, or operating risk prevents responsible change.
- Time to value
- Data risk
- Reversibility
- Maintenance ownership
Plan implementation and launch
Sequence work around rAG, fine-tuning, and tool use, protect users affected by token or GPU costs undermine the business model, and define the point at which 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, document the result, and make responsibility explicit. 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 and what happens next.
- Acceptance evidence
- Current documentation
- Monitoring owner
- Prioritized next step