Abstract: The application of large language models (LLMs) in automating job posting extraction and generating personalized cold emails offers a promising approach to improving the efficiency and effectiveness of recruitment and marketing processes. The increasing reliance on email marketing in sales outreach and HR assistance has created a need for more efficient and effective email generation tools. Traditional methods of crafting personalized emails can be time-consuming and labor-intensive, leading to decreased productivity and efficiency. A prompt-based approach is used to fine-tune the LLM, enabling it to accurately extract relevant information from job postings, including job title, experience, skills, and description. The model leverages the LLM's ability to process unstructured data and generate human-like text, producing cold emails tailored to the specific needs of clients. The results demonstrate that the model achieves high accuracy in extracting job posting information and generating relevant cold emails, indicating its potential to improve response rates, conversion rates and provide personalized email content.

Key words: large language models, personalized emails, automation