AI Systems Foundations for Software Factory Architects
Explain only the AI concepts that change factory architecture, cost, security, evaluation, or operations decisions.
A focused view of boundaries, contracts, state, authority, failure paths, and tradeoffs drawn from this chapter.
4. Model classes without architecture theater
General-purpose language models, code-specialized profiles, small local models, reasoning-oriented profiles, embedding models, rerankers, classifiers, and multimodal models are capability classes. Select by representative evaluation, data path, latency, cost, availability, tool and schema support, context behavior, and operational constraints. Model family names and internal architectures change faster than these decisions.
8. Failure and fallback
| Failure | Correct response |
|---|---|
| Context limit | Recompile with explicit allocation; never silently drop governing content |
| Invalid structured output | Bounded schema repair or fail with original response retained |
| Provider unavailable | Approved equivalent fallback or explicit pause; record changed profile |
| Quality drift | Restrict profile, route to previous version, investigate by slice |
| Cost spike | Admission and budget control; do not bypass safety validation |
| Suspected data leakage | Contain, revoke credentials, preserve evidence, notify data/security owners |
| Nonconverging loop | Stop on attempt/improvement budget and escalate |
9. Tradeoffs and nonclaims
Larger models may improve difficult tasks but increase cost, latency, and data exposure. Local models can improve control but add serving, evaluation, and capacity work. Adaptation may improve repeated domain behavior but raises data and rollout obligations. Choose the simplest qualified mechanism. This primer does not teach model training mathematics or certify any model or provider.
Review this chapter.
Challenge a claim, boundary, missing failure mode, unclear term, or unsupported evidence statement.
- Claim
- Boundary
- Failure
- Evidence