Model tier trade-offs
Selecting among relative capability tiers (fast/cheap, balanced, highest-capability) by matching task difficulty and risk to an accuracy floor plus latency and unit-cost SLAs at expected volume.
Exam context: Always-pick-largest is usually wrong — choose the smallest clearing tier.
See also: 2.1 Model selection
System prompt criteria
Explicit acceptance criteria, required output formats, and boundary conditions in the system prompt — stronger than vague style advice alone.
Exam context: Vague encouragement without criteria/formats fails exam-style prompts.
See also: 2.2 System prompts & templates
Deterministic vs prompt guardrails
Probabilistic prompt guidance steers behavior most of the time; deterministic controls (tool-path blocks, allowlists, policy checks) enforce must-always / must-never outcomes.
Exam context: Hard safety rules need enforcement beyond the system prompt.
See also: 2.2 System prompts & templates
Zero-shot prompting
Instructing the model with a clear task and rubric without demonstrations — appropriate when the specification alone is enough.
Exam context: Prefer zero-shot for clear, well-specified tasks before heavier techniques.
See also: 2.3 Prompt techniques
Few-shot prompting
Providing a small set of high-quality exemplars so the model learns desired format or edge-case behavior.
Exam context: Few-shot targets format drift and edge failures — not every problem.
See also: 2.3 Prompt techniques
Chain-of-thought (CoT)
Asking for explicit multi-step reasoning (or a scratchpad) when those extra tokens improve measured accuracy enough to justify latency and cost.
Exam context: Do not default CoT on simple latency-sensitive tasks.
See also: 2.3 Prompt techniques
Context budget
An explicit allocation of tokens across system instructions, retrieval, tool results, and history — with headroom for the response — instead of unbounded dumps.
Exam context: Dumping entire corpora into context is a common wrong answer.
See also: 2.4 Context & tokens
Lost-in-the-middle
A position effect where critical facts buried in the middle of a large context are easier to miss — mitigated by short live-state blocks and trimmed history.
Exam context: Place must-follow constraints and IDs carefully; avoid giant undifferentiated pastes.
See also: 2.4 Context & tokens
Prompt caching
Reusing a stable prompt prefix across requests so unchanging tokens are not fully re-billed/recomputed each turn when the platform supports it.
Exam context: Cache stable prefixes; do not treat volatile ticket text as the cache key content.
See also: 2.5 Prompt reuse
Modular prompts & Skills
Versioned shared instruction modules plus Skills (isolated reusable workflows) that keep live turns lean — avoiding full inline playbooks every message.
Exam context: Prefer modules/Skills/caching over pasting verbose skill text every turn.
See also: 2.5 Prompt reuse