AI Today Podcast: Artificial Intelligence Insights, Experts, and Opinion AI & Data Today

As folks continue to use LLMs, best practices are emerging to help users get the most out of LLMs. OpenAI's ChatGPT allows users to tailor responses to match their tone and desired output goals. Many have reported that using custom instructions results in much more accurate, precise, consistent, and predictable results. But why would you want to do this and why does it matter? In this episode, hosts Kathleen Walch and Ron Schmelzer discuss why this is a best practice.








What are custom instructions in ChatGPT?







In ChatGPT, custom instructions are provided by answering two questions asked in settings that get sent along with your prompts:










What would you like ChatGPT to know about you to provide better responses?







How would you like ChatGPT to respond?









It's important to note that once created, these instructions will apply to all future chat prompt sessions (not previous or existing ones). This allows you to make somewhat permanent settings that don’t have to be constantly reset. Custom prompt instructions are short and generally limited to about 1500 characters, so keep it precise and concise.








Show Notes:










Free Intro to CPMAI course







CPMAI Certification







Subscribe to Cognilytica newsletter on LinkedIn







Properly Scoping AI Projects [AI Today Podcast]







Prompt Engineering Best Practices: What is Prompt Chaining? [AI Today Podcast]







AI Today Podcast: AI Glossary Series – OpenAI, GPT, DALL-E, Stable Diffusion







AI Today Podcast: AI Glossary Series – Tokenization and Vectorization

Companies of all sizes in every industry are looking to see how Artificial Intelligence (AI), machine learning (ML), and cognitive technology projects can provide them a competitive edge. They want to provide efficiencies and improve ROI in today’s competitive landscape. As a result, this creates tremendous opportunity in the field of AI for professionals who are CPMAI certified and follow the CPMAI methodology. This globally recognized certification helps you stand out from the pack when it comes to scoping, managing, and understanding AI and ML projects. In this episode of the AI Today podcast we interview George Fountain. He is Senior Project Manager at Booz Allen Hamilton (BAH), and is CPMAI certified.








What is CPMAI?







Staying up to date on AI in today's rapidly changing world is critical. However, just reading the news about AI versus actually being able to apply AI best practices to projects are two different things. As a senior project manager, George needs to have a step by step framework to successfully run and manage client projects. With the recent buzz around GenAI this has now become more important than ever. CPMAI is providing the framework needed. George shares how he is applying CPMAI in the real world.








Is the CPMAI certification worth it?







When it comes to investing your time and money into certifications you want to make sure it's worth it. For George, CPMAI has been a transformational certification to his career. It's helped him grow key skills including providing the knowledge, lexicon, and credibility he was after. George shares his feedback and experience on this podcast. He wraps up the discussion by sharing how he believes AI will impact organizations in the years ahead.








Show Notes:










CPMAI Training and Certification







Cognilytica newsletter







Applying CPMAI Methodology to AI Projects: Interview with Christina Kucek, CAI [AI Today Podcast]

To improve the reliability and performance of LLMs, sometimes you need to break large tasks/prompts into sub-tasks. Prompt chaining is when a task is split into sub-tasks with the idea to create a chain of prompt operations. Prompt chaining is useful if the LLM is struggling to complete your larger complex task in one step. In this episode of the AI Today podcast hosts Kathleen Walch and Ron Schmelzer discuss prompt chaining. This is part 2 in our 6 part series on Prompt Engineering Best Practices.








Why do you need to chain the prompts?







In this episode we explain when and why to use prompts. Chaining prompts are great, and is a best practices approach, when you're answering questions from information in a document. It's also good when you want to validate & refine Responses. Or, when you're looking to Simplify Writing long-form content and break the writing process into outlined sections or chapters that the AI can expand upon in sequence. Prompt chaining is also good for providing a stepwise approach to research projects. And, for anything that needs an iterative approach such as iterative data analysis, iterative Approach to Computer programming, and iterative approach to other process-based tasks.








What is the prompt chaining technique?







There are a few different types of chain prompts. We go over Chain-of-thought prompting which is breaking down big tasks. We also discuss Self-consistency and ReAct techniques which is thinking harder and smarter. And, we discuss Flipped Interaction as well as Question Refinement Pattern.








When looking to chain prompts there are some best practices to follow. These best practices include decomposing the task, crafting and testing your prompts, chaining prompts, and evaluate and iterate as needed. Your prompt won't be perfect on the first try. Like we always say: Think Big, Start Small, and Iterate Often.








This is part 2 in our 6 part series on Prompt Engineering Best Practices. Subscribe to AI Today to get notified of upcoming episodes in this series and learn the best practices for prompt engineering from Cognilytica AI thought leaders.








Show Notes:










Free Intro to CPMAI course







CPMAI Certification







Subscribe to Cognilytica newsletter on LinkedIn







Properly Scoping AI Projects [AI Today Podcast]

LLMs are basically big “text predictors” that try to generate outputs based on what it expects is the most likely desired output based on what the user provides as the text-based input, the prompt. Prompts are natural language instructions for an LLM provided by a human so that it will deliver the desired results you’re looking for. In this episode of the AI Today podcast hosts Kathleen Walch and Ron Schmelzer dig into using a prompt pattern. They explain what prompts are, why good prompts are so important, and why using prompt patterns are a prompt engineering best practice.








What is prompt patterns?







To ensure your prompt contains all essential elements for optimal results, adopt a structured "formula" approach. Most of the popular prompt pattern formulas for creating prompts include aspects of the following: Act as a [ROLE], Performing a [TASK], Responding in a [FORMAT]. There are many different prompt patterns out there, and the right one for you depends on your needs. There are a number of considerations for determining which Prompt Engineering pattern or formula to consider. In the podcast we discuss the RTP and CREATE patterns and provide examples of when to use both prompt patterns.








This is part 1 in our 6 part series on Prompt Engineering Best Practices. Subscribe to AI Today to get notified of upcoming episodes in this series and learn the best practices for prompt engineering from Cognilytica AI thought leaders.








Show Notes:










Free Intro to CPMAI course







CPMAI Certification







Subscribe to Cognilytica newsletter on LinkedIn







Properly Scoping AI Projects [AI Today Podcast]

AI is helping to re-imagine experiences of all sorts, including air travel. At the 2024 SXSW Conference and Festivals Bernadette Berger, who is Director of Innovation at Alaska Airlines presents on “The Sky's the Limit: How AI will Re-imagine Airports”. In this episode of the AI Today podcast hosts and AI thought leaders Kathleen Walch and Ron Schmelzer have the opportunity to interview Bernadette.








How is AI transforming the aviation industry?







Transforming industries, especially ones that have been around for a long time, is not always easy. Yet, AI is transforming the aviation industry and passenger experience in some profound ways. From facial recognition technology to help identify passengers to predictive analytics to help with logistics and routing, aviation and travel are already seeing the benefits of AI. Additionally, if you add personalized digital assistants and real-time navigation airports will become fully accessible and immersive hubs for travel, retail and entertainment. Bernadette shares with our listeners what this AI-enabled vision will look like.








How is AI affecting the airline industry?







Already impacting passenger and customer experiences, AI is only going to continue to have an impact. Bernadette provides examples of what's currently happening and how AI will continue to transform these experiences in the future. But, with any technology the good comes with its challenges. Bernadette shares how AI will Re-imagine Air Travel. She also shares some of the challenges with AI adoption within the transportation industry. And, what can be done to help with these challenges both now and in the future.










This podcast was recorded as part of the AI Today @ SXSW 2024 podcast activation. This podcast is sponsored by Intel, the spark for the dreamers who do. They dream of a life with no diseases, of cleaner, greener, more reliable energy  of advancing education by bringing AI everywhere.  Intel is the spark to start something new. To know that no dream is too daring when you have the right foundation. It starts with Intel. Learn more at Intel.com/starts










Show Notes:










Free Intro to CPMAI course







CPMAI Certification







Subscribe to Cognilytica newsletter on LinkedIn







Intel







Intro to Trustworthy AI course

AI is having an impact on every industry, including healthcare. AI's impact in the practice of medicine is helping to reshape the practice of medicine both now, and in the future. In this episode of the AI Today podcast we interview Dr. Jag Singh. He is a Professor of Medicine at Harvard Medical School, focusing on the application of AI in the practice of medicine. He is also a practicing cardiologist at Mass General Brigham (MGB). He also recently presented at the 2024 SXSW Conference and Festivals on this topic as well.








What is the impact factor of AI in medicine?







Hyperpersonalization, one of the 7 patterns of AI, is being applied to healthcare. Dr. Singh shares how he sees AI impacting personalized healthcare both now and in the years ahead. Additionally, he shares how virtual care, the role of sensors, and other AI technologies will impact care for patients in the years ahead.








Dr. Singh also shares insights on what he sees as the most likely application for AI in healthcare in the short term and long term. As well as the critical success factors for AI's impact in the practice of medicine. He wraps the podcast on an inspirational note with what he believes the future of AI is in general and its application to organizations and beyond.










This podcast was recorded as part of the AI Today @ SXSW 2024 podcast activation. This podcast is sponsored by Intel, the spark for the dreamers who do. They dream of a life with no diseases, of cleaner, greener, more reliable energy  of advancing education by bringing AI everywhere.  Intel is the spark to start something new. To know that no dream is too daring when you have the right foundation. It starts with Intel. Learn more at Intel.com/starts










Show Notes:










Free Intro to CPMAI course







CPMAI Certification







Subscribe to Cognilytica newsletter on LinkedIn







Intel







Intro to Trustworthy AI course







Future Care: Sensors, Artificial Intelligence, and the Reinvention of Medicine by Dr. Jag Singh