AI Con USA 2025 - Prompt Engineering
Monday, June 9
Become an AI Power User
NewImpostering a bit in the AI-verse? Overwhelmed by daily AI announcements? Unsure you're using AI most effectively? Tiny bit of FOMO? We've got you covered! In this workshop, we'll help you become an AI Power User. Become a boss at your job, whatever your role or industry! We'll show you where AI shines and where you'll want to be careful, plus toss you lots of hands-on practice. In our time together, we'll help you pinpoint YOUR niche, build a custom AI assistant, and develop a comms strategy to show off your new skills. You'll walk out with cutting-edge knowledge, a prompt library...
Tuesday, June 10
Prompt Engineering for Software Practitioners
With the sudden rise of ChatGPT and large language models (LLMs), practitioners are using these tools for all aspects of engineering. This includes leveraging LLMs for creating software artifacts such as requirements documents, source code, and tests; reviewing them for issues and making corrective suggestions, and analyzing or summarizing results or outcomes. However, if LLM's are not fed good prompts describing the task that the AI is supposed to perform, their responses can be inaccurate and unreliable. Join Tariq King as he teaches you how to craft high-quality AI prompts and...
A Quality Engineering Introduction to AI and Machine Learning
Although there are several controversies and misunderstandings surrounding AI and machine learning, one thing is apparent — people have quality concerns about the safety, reliability, and trustworthiness of these types of systems. Not only are ML-based systems shrouded in mystery due to their largely black-box nature, they also tend to be unpredictable since they can adapt and learn new things at runtime. Validating ML systems is challenging and requires a cross-section of knowledge, skills, and experience from areas such as mathematics, data science, software engineering, cyber-security,...
Wednesday, June 11
Testing Powered by AI/ML Synthetic Test Data: A Game Changer
The session covers a testing approach that utilizes a new age Test Data Management (TDM) technique which learns from production data. Machine learning analyzes the data to create a model capable of generating synthetic data with identical source data attributes. Multiple learning iterations refine the model, enhancing its accuracy with each cycle. The data model incorporates security measures like differential privacy, enabling safe movement to lower environments. Later, generative AI leverages this model to produce desired volumes of test data for various testing types, including...
Moving from Reproducible Data Science to the World of LLM, GPT, RAG, and Hallucination
We are moving from the world of reproducible data science (machine learning, area under the curve, a/b testing, model selection, model testing, model fitting and model drift to find the best way to run a data science organization) to being faced with the "Easy Button." This "Easy Button" is often wrong, has poor data protocols, and tries to please; without factual basis. Join Bob to explore ways to make the world better in the bridge from science to the new world of utterances. This session will include examples and experiences from IBM Watson, AI at GE Healthcare, AI at Microsoft, and...
Mastering Prompt Engineering: Unlocking the Power of Prompt Patterns and Meta-Prompting Technique
As AI becomes increasingly central to professional workflows, the ability to craft effective prompts has emerged as a crucial skill. While many users struggle with trial-and-error approaches, this session introduces a systematic framework for prompt engineering based on proven patterns and and meta-prompting techniques. You'll learn how to apply specific prompt patterns such as persona, meta-language creation, and flipped interaction, and more, to achieve consistently better results. You will then explore meta-prompting - a powerful technique where AI assists in generating, refining, and...
Thursday, June 12
Mastering Quality Engineering in the Age of AI
PreviewAI is transforming the software quality engineering industry by automating routine tasks and enabling smarter testing approaches. Its integration into software development has profoundly reshaped the field of Quality Engineering (QE). This presentation explores the evolving role of QE in the AI era, addressing the challenges, opportunities, and strategies for achieving excellence in AI-driven environments. By examining the intersection of AI and QE, it provides valuable insights into best practices for maintaining high-quality software and equips QE professionals with the knowledge...
RAG Has Evolved - Enhance Your RAG Pipeline with These Concepts
The majority of businesses today can set up a fundamental RAG pipeline that effectively handles most use cases. However, this basic setup eventually reaches its limitations in terms of functionality and accuracy, hindering further advancements. Matt Payne aims to detail the necessary pipeline components for building advanced RAG pipelines. For each component, he will explain the what, when, why, and how and provide real-world examples. Key areas of focus include leveraging tools and function calling, which enables you to create a systematic approach to using knowledge from multiple sources...