Prompt engineering for generative artificial intelligence chatbots in health research: A practical guide for traditional, complementary, and integrative medicine researchers
Bibliographic record
Abstract
Generative artificial intelligence (GenAI) chatbots powered by large language models (LLMs) are increasingly used in health research to support a range of academic and clinical activities. While increasingly adopted in biomedical research, their application in traditional, complementary, and integrative medicine (TCIM) remains underexplored. TCIM presents unique challenges, including complex interventions, culturally embedded practices, and variable terminology. This article provides a practical, evidence-informed guide to help TCIM researchers engage responsibly with GenAI chatbots through prompt engineering, the design of clear, structured, and purposeful prompts to improve output relevance and accuracy. The guide outlines strategies to tailor GenAI chatbot interactions to the methodological and epistemological diversity of TCIM. It presents use cases across the research process, including research question development, study design, literature searches, selection of reporting guidelines and appraisal tools, quantitative and qualitative analysis, writing and dissemination, and implementation planning. For each stage, the guide offers examples and best practices while emphasizing that AI-generated content should always serve as a starting point, not a final product, and must be reviewed and verified using credible sources. Potential risks such as hallucinated outputs, embedded bias, and ethical challenges are discussed, particularly in culturally sensitive contexts. Transparency in GenAI chatbot use and researcher accountability are emphasized as essential principles. While GenAI chatbots can expand access to research support and foster innovation in TCIM, they cannot substitute for critical thinking, methodological rigour, or domain-specific expertise. Used responsibly, GenAI chatbots can augment human judgment and contribute meaningfully to the evolution of TCIM scholarship.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.021 | 0.042 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.003 | 0.005 |
| Scholarly communication | 0.008 | 0.008 |
| Open science | 0.005 | 0.008 |
| Research integrity | 0.006 | 0.006 |
| Insufficient payload (model declined to judge) | 0.021 | 0.017 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".