Prompt engineering techniques: from basic to advanced patterns

Prompt engineering is the practice of designing inputs to LLMs to produce desired outputs. As models improve, prompting becomes more powerful and more nuanced. Good prompting is the difference between an AI that produces useful output and one that produces plausible nonsense.

The foundation is a clear, specific prompt. Tell the model exactly what you want, in what format, and with what constraints. Include examples of good output. Specify what the model should do if it doesn't know the answer. A vague prompt produces unpredictable output.

Chain-of-thought prompting asks the model to reason step by step before giving the final answer. This dramatically improves performance on reasoning tasks like math problems, logical puzzles, and multi-step analysis. Simply adding "Let's think step by step" to your prompt often improves results.

Few-shot prompting provides examples of the desired input-output pattern in the prompt. Include 2-5 examples covering different cases. The examples should be representative of the range of inputs you expect. Few-shot prompting is often more effective than zero-shot for complex tasks.

Structured output formats ensure the model's response is machine-parseable. Ask for JSON, YAML, or markdown output with specific fields. Provide a schema or template. Structured output makes it easy to validate and process the model's response programmatically.

System prompts set the model's behavior and persona. "You are a helpful assistant" is a start, but more specific personas produce better results. "You are a senior software engineer reviewing code" produces more useful code feedback than a generic system prompt.

Prompt chaining breaks complex tasks into a series of simpler prompts. Each prompt's output feeds into the next. Chaining is more reliable than asking the model to do everything in one prompt because each step is simpler and more focused.

Iterate on your prompts systematically. Change one thing at a time. Test with a consistent set of inputs. Measure output quality against your criteria. Prompt engineering is an empirical discipline what works best is determined by testing, not intuition.

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Rizwan Saleem | https://rizwansaleem.co