Glossary
Prompt Engineering
Designing inputs to LLMs that reliably produce desired outputs.
Definition
Prompt engineering is the discipline of designing the text inputs to large language models — including instructions, examples, formatting cues, and constraints — to produce reliable, on-spec outputs. It includes techniques like few-shot prompting, chain-of-thought, structured output, and role conditioning.
Context
Prompt engineering is often dismissed as 'just writing instructions' but is, in practice, the largest single lever on production AI quality. Strong prompts often eliminate the need for fine-tuning. As models improve, prompts get shorter — but the rigor of designing, testing, and versioning prompts remains essential.
Related terms
Large Language Model (LLM)A neural network trained on massive text corpora to predict next tokens.Read Fine-TuningContinued training of a base model on task-specific data.Read AI Evaluation (Evals)The practice of systematically measuring AI system quality.Read Retrieval-Augmented Generation (RAG)Injecting retrieved documents into an LLM prompt to ground outputs.Read