Prompt Engineering is the practice of designing and refining text inputs to guide generative AI models toward specific, desired outputs. By 2026, the discip
Prompt engineering structures the model's input to shape its output: assigning a role, stating the task and constraints, supplying examples (few-shot), requesting structured formats like JSON, and ordering context so the most important information lands where the model attends to it. Modern practice extends to context engineering, programmatically assembling retrieval results, tool definitions, and memory, and uses frameworks like DSPy to optimize prompts against measurable test sets instead of intuition.
The same model can be unusable or production-grade depending on its prompt and context. Teams that treat prompts as versioned, tested artifacts ship more reliable AI features and survive model upgrades without regressions, while ad-hoc prompting quietly breaks every time a provider updates a model.
Yes, but it matured into context engineering: structuring everything the model sees, instructions, retrieved data, tool schemas, memory, rather than wordsmithing alone. The craft now lives inside AI engineering workflows with version control and evaluation.
Clear role and task framing, explicit constraints and output format, relevant examples, and only the context the task needs. Good prompts are testable: you can score outputs against criteria and detect regressions when models change.
Zero-shot gives instructions with no examples; few-shot includes worked examples in the prompt so the model imitates the pattern. Few-shot generally wins on format-sensitive or ambiguous tasks at the cost of longer prompts.