Loading
Topic
Model behavior, tokens, context, generation, limitations, and foundational applied-AI decisions.
Practice items tagged with LLM Fundamentals.
Compares managed model APIs with operating open-weight models across control, expertise, privacy, scale, latency, cost, and portability.
Separates runtime model use from model training, fine-tuning, prompt design, and retrieval-augmented generation as different engineering levers.
Explains how decoding settings reshape token selection and why they must be tuned against task-specific evaluation rather than folklore.
Balances AI quality, tail latency, and cost through explicit product thresholds, model choice, context control, caching, routing, and measurement.
Handles token-budget overflow and incomplete generation through explicit prioritization, rejection, retrieval, summarization, continuation, and validation.
Chooses a model from task-specific evaluation, operating constraints, safety, latency, cost, context, modality, and provider requirements.