The emergence of generative artificial intelligence (GenAI) offers the potential to enhance health economics and outcomes research (HEOR) by streamlining traditionally time-consuming and labour-intensive tasks, such as literature reviews, data extraction, and economic modelling. To effectively navigate this evolving landscape, health economists need a foundational understanding of how GenAI can complement their work. This primer aims to introduce health economists to the essentials of using GenAI tools, particularly large language models (LLMs), in HEOR projects. For health economists new to GenAI technologies, chatbot interfaces like ChatGPT offer an accessible way to explore the potential of LLMs. For more complex projects, knowledge of application programming interfaces (APIs), which provide scalability and integration capabilities, and prompt engineering strategies, such as few-shot and chain-of-thought prompting, is necessary to ensure accurate and efficient data analysis, enhance model performance, and tailor outputs to specific HEOR needs. Retrieval-augmented generation (RAG) can further improve LLM performance by incorporating current external information. LLMs have significant potential in many common HEOR tasks, such as summarising medical literature, extracting structured data, drafting report sections, generating statistical code, answering specific questions, and reviewing materials to enhance quality. However, health economists must also be aware of ongoing limitations and challenges, such as the propensity of LLMs to produce inaccurate information ('hallucinate'), security concerns, issues with reproducibility, and the risk of bias. Implementing LLMs in HEOR requires robust security protocols to handle sensitive data in compliance with the European Union's General Data Protection Regulation (GDPR) and the United States' Health Insurance Portability and Accountability Act (HIPAA). Deployment options such as local hosting, secure API use, or cloud-hosted open-source models offer varying levels of control and cost, each with unique trade-offs in security, accessibility, and technical demands. Reproducibility and transparency also pose unique challenges. To ensure the credibility of LLM-generated content, explicit declarations of the model version, prompting techniques, and benchmarks against established standards are recommended. Given the 'black box' nature of LLMs, a clear reporting structure is essential to maintain transparency and validate outputs, enabling stakeholders to assess the reliability and accuracy of LLM-generated HEOR analyses. The ethical implications of using artificial intelligence (AI) in HEOR, including LLMs, are complex and multifaceted, requiring careful assessment of each use case to determine the necessary level of ethical scrutiny and transparency. Health economists must balance the potential benefits of AI adoption against the risks of maintaining current practices, while also considering issues such as accountability, bias, intellectual property, and the broader impact on the healthcare system. As LLMs and AI technologies advance, their potential role in HEOR will become increasingly evident. Key areas of promise include creating dynamic, continuously updated HEOR materials, providing patients with more accessible information, and enhancing analytics for faster access to medicines. To maximise these benefits, health economists must understand and address challenges such as data ownership and bias. The coming years will be critical for establishing best practices for GenAI in HEOR. This primer encourages health economists to adopt GenAI responsibly, balancing innovation with scientific rigor and ethical integrity to improve healthcare insights and decision-making.