Core Prompting Techniques

Prompt engineering is the practice of designing inputs that reliably elicit high-quality outputs from language models. At the professional level, it involves systematic techniques, not trial and error.

Zero-shot, one-shot, and few-shot prompting

Zero-shot: the task description alone, with no examples. Works for clear, common tasks. "Classify this customer review as positive, negative, or neutral."

One-shot/few-shot: including one or more examples of the desired input-output pattern before the actual task. Dramatically improves performance for uncommon formats, domain-specific tasks, and nuanced judgements. The examples do not need to be real — synthetic examples that demonstrate the format work equally well.

Chain of thought (CoT)

Adding "Let us think through this step by step" or providing reasoning traces in few-shot examples significantly improves performance on complex reasoning tasks. The model is effectively forced to generate intermediate steps before producing its answer, which reduces errors on multi-step reasoning.

Role prompting

Assigning the model a persona ("You are a senior tax accountant in New Zealand") activates relevant knowledge and appropriate register. The mechanism is not fully understood — it appears to shift what the model treats as the relevant distribution to sample from — but it works reliably in practice.

Structured output formatting

For programmatic use, specifying the exact output format — JSON schema, table structure, XML tags — is essential. Most frontier models support JSON mode or structured output APIs that force output to conform to a specified schema, dramatically reducing parsing errors in downstream code.

The highest-leverage prompting investment is usually few-shot examples and output format specification. These two changes resolve the majority of prompt quality problems in production systems.