Internship LLM Patent Intelligence

优缺点分析

角色解读

• This internship provides hands-on experience with cutting-edge LLM technology in a corporate setting, building a strong foundation for careers in AI engineering or data science.
• You will develop expertise in applying LLMs to niche domains like patent intelligence, a valuable skill for IP analytics and innovation management roles.
• The role offers exposure to global business functions, potentially leading to future opportunities in AI product management or consulting.
• You will design concepts for LLM-generated reports based on patent data, addressing needs of R&D, sales, and marketing.
• You will evaluate and benchmark various large language models to select the best one for specific use cases.
• You will refine prompt engineering techniques to improve output quality and reliability.
• You will create best-practice guides to help internal teams leverage LLMs effectively and responsibly.
• Solid understanding of AI and machine learning, particularly LLMs like ChatGPT.
• Obligatory experience with intellectual property topics, especially patents.
• Ability to critically evaluate AI-generated content for accuracy.
• Strong communication skills to summarize technical information clearly.

申请策略

• Tailor your CV to explicitly mention both AI/LLM skills and patent/IP experience, as the patent requirement is obligatory.
• Research Henkel's business segments and how patents contribute to their innovation strategy, and mention this in your cover letter.
• Highlight any coursework or projects involving LLMs, NLP, or machine learning, especially if you have hands-on experience with prompt engineering.
• Emphasize your knowledge of intellectual property, including any academic or professional exposure to patents.
• Showcase your analytical and evaluation skills, such as comparing model outputs or assessing content quality.
• Demonstrate effective communication by presenting technical topics clearly, possibly through published articles or presentations.
• Familiarize yourself with popular LLM APIs and prompt engineering techniques, such as few-shot prompting and chain-of-thought.
• Learn basics of patent databases and patent analysis methods (e.g., patent classification, claims structure).

面试指南

• Use the STAR method for behavioral questions: Situation, Task, Action, Result.
• For technical questions, explain your reasoning step-by-step, covering problem understanding, approach, trade-offs, and validation.
• Show your familiarity with LLM limitations and your ability to mitigate risks through evaluation and prompt refinement.
• Can you explain how large language models work and give an example of how you would use prompt engineering to generate accurate patent summaries?
• Describe your experience with patents or intellectual property. How would you ensure the information contained in a patent is correctly represented in an AI-generated report?
• How would you evaluate the quality of an LLM's output for a business report? What metrics or methods would you use?
• How do you handle situations where AI-generated content contains hallucinations or inaccuracies?
• Why are you interested in combining AI and patent intelligence, and what do you hope to learn from this internship?