Prompt Engineering for Research

Also known as: Prompt Design, AI Prompting, LLM Instructions

The discipline of designing and optimizing instructions (prompts) given to LLMs to obtain high-quality outputs for research tasks.

Prompt Engineering is the practice of designing, structuring, and optimizing the instructions (prompts) provided to language models to obtain precise, relevant, and useful outputs. In the context of research, it is the discipline that determines how well an LLM can assist with tasks such as coding, analysis, or report generation.

Key prompt engineering techniques applied to research: chain-of-thought prompting (asking the model to reason step by step before concluding), few-shot prompting (including examples of desired outputs), role prompting (assigning the model the role of 'consumer insights expert'), and structured output prompting (requesting responses in JSON or tabular format).

The quality of prompt engineering is the difference between an LLM that produces generic and imprecise outputs and one that generates genuinely actionable insights. It is an emerging critical competency for research teams adopting AI.

Atlantia has developed specialized prompt libraries for the most common tasks in LATAM market research.