The Prompt Desk

• What we learned about LLM’s in a year

  In this one year anniversary episode of the podcast, your show hosts look back on what they have learned and observed over the past year. They made the following observations:Reflection on a year of LLM development: The hosts discuss how the LLM ecosystem has rapidly evolved over the past year, moving from initial hype to a focus on practical applications with demonstrable ROI.Changes in prompt engineering: They note a shift from instruction-based prompting to example-based prompting, finding the latter more reliable.Impact on software development: LLMs have significantly increased productivity and are changing how software is architected and developed, making previously complex tasks much simpler and faster to implement.Shifting skill requirements: The hosts predict that soft skills will become more valuable for engineers, while specific programming language knowledge may become less crucial due to AI assistance.Evolving perspectives: Both hosts describe how their views on LLMs have changed, moving from initial skepticism to greater optimism about the technology's potential and impact.Future of the field: They emphasize the transformative nature of LLMs on the entire field of computer science and encourage newcomers to stick with it, predicting that those who do will become the experts shaping the future of technology.---Continue listening to The Prompt Desk Podcast for everything LLM & GPT, Prompt Engineering, Generative AI, and LLM Security.Check out PromptDesk.ai for an open-source prompt management tool.Check out Brad’s AI Consultancy at bradleyarsenault.meAdd Justin Macorin and Bradley Arsenault on LinkedIn.Hosted by Ausha. See ausha.co/privacy-policy for more information.

  --------  

  21:13
• Validating Inputs with LLMs

  In this thought-provoking episode, AI experts Bradley Arsenault and Justin Macorin delve into the cutting-edge world of input validation using large language models (LLMs). The hosts explore how this innovative approach is transforming traditional data validation methods in software applications.The conversation begins with a look at conventional input validation techniques, primarily relying on regular expressions (RegEx) for basic text field checks. Justin then introduces the concept of using LLMs to validate more complex inputs, such as job titles or descriptions, which go beyond simple character-level validation.Throughout the episode, the hosts discuss the numerous benefits of LLM-based validation, including improved downstream results in AI systems, reduction in nonsensical inputs, and the potential to decrease reliance on customer support teams. They also tackle the implementation challenges, such as increased computation costs and the risk of rejecting valid inputs in edge cases.Bradley and Justin provide insights into the technical aspects of implementing LLM validation, discussing where in the software architecture to place these checks and how to handle user feedback. They also explore future improvements to the system, such as incorporating probability thresholds and enhancing error messages with helpful examples.The episode concludes with a balanced view of the trade-offs involved in adopting this new approach. While acknowledging the potential for user resistance and increased complexity, the hosts argue that the long-term benefits of cleaner data and reduced errors make LLM-based validation a promising frontier in software development.This episode offers valuable insights for developers, data scientists, and product managers looking to enhance their applications' data quality and user experience through advanced AI techniques.---Continue listening to The Prompt Desk Podcast for everything LLM & GPT, Prompt Engineering, Generative AI, and LLM Security.Check out PromptDesk.ai for an open-source prompt management tool.Check out Brad’s AI Consultancy at bradleyarsenault.meAdd Justin Macorin and Bradley Arsenault on LinkedIn.Hosted by Ausha. See ausha.co/privacy-policy for more information.

  --------  

  23:06
• Why you can't automate everything with LLMs

  In this episode of Prompt Desk, hosts Bradley Arsenault and Justin Macorin dive into the evolving landscape of AI applications. They explore the shift from full automation dreams to the reality of human-AI collaboration, discussing why guided experiences are proving more effective than autonomous systems. The hosts share insights on designing AI interfaces, managing error rates, and the importance of human oversight in complex AI tasks. Whether you're an AI enthusiast, developer, or business leader, this episode offers valuable perspectives on harnessing the power of large language models while avoiding common pitfalls. Join Bradley and Justin as they unpack the challenges and opportunities in creating reliable, user-friendly AI applications that truly augment human capabilities.---Continue listening to The Prompt Desk Podcast for everything LLM & GPT, Prompt Engineering, Generative AI, and LLM Security.Check out PromptDesk.ai for an open-source prompt management tool.Check out Brad’s AI Consultancy at bradleyarsenault.meAdd Justin Macorin and Bradley Arsenault on LinkedIn.Please fill out our listener survey here to help us create a better podcast: https://docs.google.com/forms/d/e/1FAIpQLSfNjWlWyg8zROYmGX745a56AtagX_7cS16jyhjV2u_ebgc-tw/viewform?usp=sf_linkHosted by Ausha. See ausha.co/privacy-policy for more information.

  --------  

  18:24
• Data Preparation Best Practices for Fine Tuning

  In this episode of The Prompt Desk podcast, hosts Bradley Arsenault and Justin Macorin dive deep into the world of fine-tuning large language models. They discuss:The evolution of data preparation techniques from traditional NLP to modern LLMsStrategies for creating high-quality datasets for fine-tuningThe surprising effectiveness of small, well-curated datasetsBest practices for aligning training data with production environmentsThe importance of data quality and its impact on model performancePractical tips for engineers working on LLM fine-tuning projectsWhether you're a seasoned AI practitioner or just getting started with large language models, this episode offers valuable insights into the critical process of data preparation and fine-tuning. Join Brad and Justin as they share their expertise and help you navigate the challenges of building effective AI systems.---Continue listening to The Prompt Desk Podcast for everything LLM & GPT, Prompt Engineering, Generative AI, and LLM Security.Check out PromptDesk.ai for an open-source prompt management tool.Check out Brad’s AI Consultancy at bradleyarsenault.meAdd Justin Macorin and Bradley Arsenault on LinkedIn.Hosted by Ausha. See ausha.co/privacy-policy for more information.

  --------  

  20:26
• Multilingual Prompting

  Have you ever tried to run a prompt in another language and gotten completely different results? It is a sad truth that our LLM models continue to work best in English. It is something that will probably always remain true because of the dominance of the English language, which ensures that a disproportionate amount of the worlds data is found in English.In this episode of The Prompt Desk, your hosts Justin Macorin and Bradley Arsenault discuss their relatively brief experiences with trying to get prompts working well in other languages, and provide some tips and tricks based on what they have learned so far.---Continue listening to The Prompt Desk Podcast for everything LLM & GPT, Prompt Engineering, Generative AI, and LLM Security.Check out PromptDesk.ai for an open-source prompt management tool.Check out Brad’s AI Consultancy at bradleyarsenault.meAdd Justin Macorin and Bradley Arsenault on LinkedIn.Please fill out our listener survey here to help us create a better podcast: https://docs.google.com/forms/d/e/1FAIpQLSfNjWlWyg8zROYmGX745a56AtagX_7cS16jyhjV2u_ebgc-tw/viewform?usp=sf_linkHosted by Ausha. See ausha.co/privacy-policy for more information.

  --------  

  15:39

Plus de podcasts Technologies

Podcasts tendance de Technologies

À propos de The Prompt Desk