Prompt Engineering
The practice of designing model inputs to produce reliable, task-specific outputs.
Prompt engineering is the fastest, cheapest lever for improving LLM output quality. A well-structured prompt — with role, task, constraints, examples, and output format — can lift accuracy 20–50% without any code change.
Core techniques include few-shot examples, chain-of-thought reasoning, structured output (JSON mode, tool calling), and system prompts that establish persona and rules.
Mature teams treat prompts as source-controlled artifacts with versioning, evaluation datasets, and regression tests — not as strings hidden in code.
Key points
- Few-shot examples typically beat zero-shot for structured tasks
- Chain-of-thought ('think step by step') improves reasoning on complex problems
- JSON schemas and tool calling eliminate most parsing errors
- Prompts break silently on model version upgrades — regression tests catch this
Common use cases
Frequently asked
Related terms
A neural network trained on massive text corpora to understand and generate human-like language.
An LLM-driven system that plans, calls tools, and executes multi-step tasks autonomously.
Continuing training of a pre-trained model on domain-specific data to specialize its behavior.
When an LLM generates plausible-sounding but factually incorrect output.
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