Tackling challenges in large language model–based data extraction via context engineering: A commentary on Jansen et al. (2025).

. Their findings indicated that while achieving acceptable-to-good accuracy for some variables describing study characteristics, LLMs struggled with numerical variables, especially those related to effect sizes. In this commentary, we discuss the current challenges of automated data extraction and potential pathways to improve the work reported in Jansen et al.'s study. We situate our discussion within the framework of context engineering, aiming to refine the information provided to LLMs through dynamic optimization strategies tailored to specific tasks. We identify five key challenges that reflect either LLMs' unique patterns or standard practices in research synthesis: parsing semistructured data, understanding long contexts, performing arithmetic induction, engaging in complex reasoning, and ensuring the reproducibility of coding protocols. We then outline potential solutions inspired by context engineering implementations such as retrieval-augmented generation and tool-integrated reasoning. For illustration, we present four examples: extracting semistructured data via optical character recognition, reliably computing effect sizes through function calls, performing adaptive retrieval with LLM-based agents, and iteratively improving outputs through self-refinement. We conclude by calling for future research in automated data extraction to advance beyond simple instruction-following paradigms toward more reliable forms of context engineering. (PsycInfo Database Record (c) 2026 APA, all rights reserved).