Recent advancements in LLMs have presented exciting prospects for automating information extraction pipelines. Fine-tuning LLMs is crucial for constructing effective pipelines, especially in materials science. However, the scarcity of pre-annotated data poses a formidable challenge Figure : Illustration of the Semi-Automated Annotation Workflow: The figure showcases the structured prompt methodology using Google's Gemini Pro language model for annotating materials science literature. The structured prompt, encompassing detailed instructions for extracting JSON or generating abstractive summaries, includes example input and desired output pairs. The provided examples, originating from literature, are human annotated gold standards. A test input is then processed by the model, and users evaluate the output's satisfaction. If satisfactory, the output is added to the example pool; otherwise, users can rerun the model or manually refine the output before inclusion in the pool. This semi-automated workflow combines human expertise with model guidance for efficient text annotation.