🧠 Prompt Engineering Language
6 exercise sets — 30 exercises. Vocabulary for systematic prompt engineering in production LLM systems.
- Advanced
System Prompt Vocabulary
System prompt vs. user message vs. assistant message, instruction following, persona instructions, system prompt confidentiality, and prompt injection defense.
- Advanced
Few-Shot & Chain-of-Thought Vocabulary
Few-shot learning, in-context learning (ICL), chain-of-thought (CoT) prompting, zero-shot CoT, self-consistency, Tree of Thoughts, and ReAct prompting.
- Advanced
Sampling Parameters Vocabulary
Temperature, top-p nucleus sampling, top-k, repetition penalty, frequency penalty, presence penalty, max tokens, and stop sequences.
- Advanced
Prompt Chaining Vocabulary
Prompt chaining, sequential and parallel prompting, prompt decomposition, sub-task delegation, map-reduce prompt pattern, and prompt DAG vocabulary.
- Advanced
Prompt Testing Vocabulary
Prompt regression testing, prompt versioning, A/B prompt comparison, golden datasets, eval harnesses, PromptFoo, and Braintrust vocabulary.
- Advanced
Prompt Security Vocabulary
Prompt injection (direct vs. indirect), jailbreak, prompt leaking, goal hijacking, adversarial suffix, sandboxing LLM outputs, and input validation.
Key prompt engineering vocabulary
Prompt structure
- "The system prompt sets the assistant's persona and constraints."
- "We use few-shot examples to steer output format reliably."
- "Adding chain-of-thought improved reasoning accuracy by 18%."
Sampling & chaining
- "Lower temperature gives more deterministic, consistent outputs."
- "We decomposed the task using a prompt chain with three steps."
- "The map-reduce prompt pattern handles long documents in chunks."
Security & testing
- "Our input pipeline includes prompt injection detection."
- "We run a prompt regression suite on every model update."
- "Jailbreak attempts are logged and reviewed by the safety team."