Comprehensive field guide
AI Training Field Guide
AI Training Field Guide organizes the decisions that matter for organizations evaluating prompting, retrieval, fine-tuning, specialized training, or ground-up model development: the current workflow, ownership, implementation choices, rollout risk, and acceptance evidence.
Working artifact
AI Training journey map
Use this map to connect visible friction to the handoff, owner, and acceptance evidence that belongs to the AI Training journey.
| Journey stage | Risk to inspect | Decision to document |
|---|---|---|
| Prompt and workflow optimization | The team assumes every problem needs training | Dataset preparation and governance |
| Retrieval-augmented generation systems | Training data is noisy or legally unclear | Fine-tuning and adaptation workflows |
| Fine-tuning existing foundation models | Success has no measurable evaluation set | Evaluation harnesses and red-team testing |
Read the situation before naming the solution
The team assumes every problem needs training. Confirm who encounters it, where it occurs, and what changed before it appeared. Then distinguish the visible symptom from dependencies such as dataset preparation and governance.
- The team assumes every problem needs training
- Training data is noisy or legally unclear
- Success has no measurable evaluation set
Protect the current state
For AI Training & Deployment, 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 prompt and workflow optimization and one observable acceptance journey. Treat retrieval-augmented generation systems as a later phase unless the evidence shows it is a true dependency.
- Prompt and workflow optimization
- Retrieval-augmented generation systems
- Fine-tuning existing foundation models
Compare repair, extension, and replacement
Repair fits when the core remains sound. Extension fits when the boundary around dataset preparation and governance 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 fine-tuning and adaptation workflows. Protect the people affected by “The team assumes every problem needs training,” and define the point where rollback is safer than continuing.
- Fine-tuning and adaptation workflows
- Evaluation harnesses and red-team testing
- GPU and inference architecture
Verify and hand off
Repeat the original journey, test a nearby failure, and document the result. A successful handoff leaves organizations evaluating prompting, retrieval, fine-tuning, specialized training, or ground-up model development able to understand what changed, who owns it, and what happens next.
- Acceptance evidence
- Current documentation
- Monitoring owner
- Prioritized next step