GenAI Capabilities & Limitations — AIF-C01 Study Guide

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Included in this chapter:

Selection criterion	Larger / higher-capability FM	Smaller / distilled FM	Why it matters to the business
Accuracy / quality	Generally higher on complex, cross-domain tasks	Adequate for narrow, well-scoped tasks	Drives task success, customer satisfaction, and trust
Latency	Slower per response	Faster, better for real-time UX	Affects conversion rate and user experience
Cost	Higher per token (more spend at scale)	Lower per token	Directly drives ROI under token-based pricing
Modality	More likely multimodal (text, image, etc.)	Often single-modality	Must match the input/output the use case needs
Customization & compliance	More fine-tuning / grounding options	Simpler, fewer knobs	Determines fit for regulated or domain-specific needs

Cheat sheet

GenAI's three advantages: adaptability, responsiveness, simplicity
One foundation model generalizes across tasks with no per-task retraining
Invoke a pretrained FM through one managed API, no infrastructure or training
Hallucination: a fluent, confident answer that is factually wrong
Knowledge cutoff: the model knows nothing after its training date
Nondeterminism: the same prompt can return different answers
Temperature trades off deterministic vs. random output
Interpretability: an FM is a low-interpretability black box
Foundation models inherit training bias and degrade as data drifts
No single best model: selection balances competing factors
A smaller or distilled model can be the right business answer
Token-based pricing ties spend to model choice and prompt length
Modality must match what the use case produces and consumes
Compliance and regulatory needs can rule a model out outright
GenAI value is measured by business metrics, not model scores
Model-quality scores answer 'is it good'; business metrics answer 'is it worth it'

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Cheat sheet