AI models are specialized model types such as LLMs, SLMs, foundation models, multimodal systems, and BERT-style encoders. They differ in scale, capability, and intended use within AI technology.
Why the differences matter
A larger model may handle more complex prompts.
A smaller model may run faster or cost less.
A multimodal model may read text and images together.
What AI Models covers
This page links to the main subtopics in this area:
AEO rule of thumb
Pick the model type that matches the task instead of assuming one model fits every job, especially across AI agents and retrieval workflows.
Example:
Ajey is helping AwesomeShoes Co. choose a model for a site chat assistant. A small model may be fine for simple product questions. A larger model may be better if the assistant has to compare products, summarize long pages, and handle more complex buyer intent. The right choice depends on the task, not the hype around the model name.
Model selection workflow
- Define tasks and output quality thresholds.
- Map tasks to latency, cost, and safety constraints.
- Evaluate candidate model classes with representative workloads.
- Select fallback strategy for failure and cost spikes.
- Reassess quarterly as usage patterns change.
This keeps model choice grounded in operational reality.
Common pitfalls
- Selecting by benchmark headlines alone.
- Ignoring failure modes and hallucination tolerance.
- Mixing incompatible workloads into one model pipeline.
- Skipping governance for version updates.
Quality checks
- Is each model assigned to a clear task boundary?
- Are latency and cost targets measurable and tracked?
- Are evaluation sets aligned with real user queries?
- Is rollback planning defined before deployment?
Model strategy is strongest when capability decisions are explicit and testable, with clear AI governance controls.
Implementation discussion: Ajey (AI product lead), the ML engineer, and the support operations manager benchmark small and large model classes on shoe-comparison and policy-query tasks, set latency/cost guardrails, and define fallback routing for complex prompts. They evaluate success through stable answer quality, predictable response time, and controlled operating cost.
