This session will offer a comprehensive overview of prompt engineering techniques and best practices to build a successful FMware. Covered topics include Basics of prompting - How to talk to a FM

• Prompting patterns
• Prompt components
• Prompt structuring
• Prompt decoding strategies
• Fragility of prompts - Manual prompt-tuning lifecycle, prompt formatting and context window sensitivity, few-shot ordering and golden labels
• Prompt anti-patterns (e.g. god prompts) - How to decompose a prompt effectively for success
• Prompt output structuring and prompt debugging
• Compiling prompts for success - prompt optimization, prompt tuning, FM-based prompt mutation and evolution, DSLs for prompt optimization, Intent-based prompt calibration
• Common prompting pitfalls
• Leveraging prompt ecosystems - Introduction to prompt stores and Reddit discussions

I’m a Software Engineering Researcher at Huawei Technologies Co., based in Kingston, Ontario, Canada. My research typically adopts machine learning and mining software repositories to investigate and propose automated solutions to technical and social problems in software engineering. You can find my research work published in main software engineering venues such as TSE, EMSE, TOSEM, ICSE, and FSE. I received a Ph.D. in Computer Science from the School of Computing at Queen’s University, under the supervision of Prof. Ahmed E. Hassan. During my Ph.D. (2017-2020), I was fortunate to work with a team of talented researchers at the Software Analysis and Intelligence Lab (SAIL). I also received a master’s (2012) and bachelor’s (2009) degree in Computer Science from the Department of Informatics (Departamento de Informática) at the State University of Maringa (Universidade Estadual de Maringá).