{"doi":"10.1093/eurjcn/zvae016","title":"How to get the most out of ChatGPT? Tips and tricks on prompting","abstract":"ChatGPT is a robust language model with the ability to comprehend and generate human-like text. It can be used for many applications, including language translation, summarization, and text completion.1 The rise of ChatGPT and other generative artificial intelligence (AI) models has made artificial intelligence more accessible to a wider public. The potential impact of generative AI is also currently being increasingly explored in healthcare.2 Recent studies have discussed and demonstrated the usefulness of generative AI for healthcare and specifically nursing, and have shown that generative AI can ease the burden on healthcare professionals, increase efficiency, and lower healthcare costs, for example by helping with the development of patient information or summarizing clinical notes.3,4 As a consequence, healthcare institutions and medical companies are starting to integrate generative AI into their businesses.5 As this becomes more commonplace, more healthcare workers and researchers will be expected to work with generative AI and need to know how they can use it to its full potential. As such, healthcare workers will have to develop a new skill: prompt engineering.5 Prompts are the instructions that you enter into a large language model (LLM), such as ChatGPT, shaping the resulting output. When interacting with AI systems, it is crucial to understand how to design and refine your prompts to enhance the quality of the responses you receive. Prompt engineering is the technique used to refine prompts to obtain more desired outputs.5 This editorial aims to provide healthcare workers with practical recommendations on how to improve their prompting skills, and hence, to get the most out of their interactions with generative AI. Several studies have enumerated and discussed general principles when it comes to prompting. For example, being specific, giving appropriate context, and clearly laying out the desired goal, will improve the performance of ChatGPT.6,7 Also in the official OpenAI guide, some valuable advice with regard to prompting has been provided.8 Users are advised to write clear instructions; provide a reference text for how the output should look; split complex tasks into simpler subtasks; and give the model time to think and work with a chain of thought rather than expecting the right answer right away; use external tools; and test changes systematically.8 These recommendations, together with experiences from the authors, have led to the set-up of a step-by-step process (see Central Illustration) that can guide new users in their first prompting experiences. For each of the steps in the process, an example will be provided to illustrate the step further. In the example, we aim to extract patient-reported signs and symptoms related to a myocardial infarction. Hereby, we assume that the data entered in ChatGPT are anonymized and/or a private and secure ChatGPT connection is used. Step 1: Define the purpose of the prompt. The first step for successful prompt engineering is to define the purpose of the project. There are two main branches: (i) text creation or refinement, and (ii) text extraction or analysis. In the first case, you want to use verbs such as ‘write’ or ‘create’. In the latter case, you should use verbs such as ‘summarize’ or ‘extract’. Example: The purpose of this prompt is to extract details from text. Step 2: Start with a simple prompt. Once the use case is defined, start with the simplest prompt possible, and see how well ChatGPT performs. Based on this first attempt, there are many opportunities for ‘tuning’ the prompt to get a better outcome. Example: ‘Which experiences does this patient have regarding myocardial infarction? [Insert patient data]’. Step 3: Make the prompt more specific. The next step is to adjust your prompt to try to improve performance. Try adding context, specifying how you want outputs to look, giving examples, and adjusting the length of the prompt. Add context: You can give t","journal":"European Journal of Cardiovascular Nursing","year":2024,"id":485392,"datarank":0.0,"base_score":0.0,"endowment":0.0,"self_citation_contribution":0.0,"citation_network_contribution":0.0,"self_endowment_contribution":0.0,"citer_contribution":0.0,"corpus_percentile":null,"corpus_rank":null,"citation_count":5,"citer_count":0,"citers_with_citation_signal":0,"citers_with_endowment":0,"datacite_reuse_total":0,"is_dataset":false,"is_dataset_confidence":0.9484,"is_data_producer":false,"deposit_databanks":null,"is_oa":true,"file_count":0,"downloads":0,"has_version_chain":false,"published_date":"2024-01-01","fair_score":null,"fair_percentile":null,"algorithm_id":"datarank_citation_only_1hop_v6","ranking_scope":"data_only","authors":[{"id":708473,"name":"Joshua Davis","orcid":"0000-0001-7324-6018","position":1,"is_corresponding":false},{"id":635988,"name":"Philip Moons","orcid":"0000-0002-8609-4516","position":2,"is_corresponding":false},{"id":1201463,"name":"Liesbet Van Bulck","orcid":"0000-0001-8975-4455","position":3,"is_corresponding":false},{"id":1201464,"name":"Brigitte N. Durieux","orcid":"0000-0001-6036-1420","position":0,"is_corresponding":true}],"reference_count":8,"raw_metadata":null,"created_at":"2026-07-19T02:07:47.633574Z","pmid":"38309697","pmcid":null,"fwci":null,"citation_percentile":null,"influential_citations":0,"oa_status":null,"license":null,"views":0,"total_file_size_bytes":0,"version_count":0,"fair_f":null,"fair_a":null,"fair_i":null,"fair_r":null,"fair_zscore":null,"fair_rationale":null,"fair_model":null,"fair_agent_version":null,"fair_fulltext_source":null,"fair_has_llm":null,"fair_computed_at":null,"clinical_trials":[],"software_tools":[],"db_accessions":[],"linked_datasets":[],"topics":[]}