{"id":1477,"date":"2026-02-03T15:08:21","date_gmt":"2026-02-03T15:08:21","guid":{"rendered":"https:\/\/datadecoded.com\/ldn\/?post_type=seminar&p=1477"},"modified":"2026-02-04T21:30:10","modified_gmt":"2026-02-04T21:30:10","slug":"building-generative-ai-applications-the-tricks-and-tips-you-need-to-avoid-common-pitfalls","status":"publish","type":"seminar","link":"https:\/\/datadecoded.com\/ldn\/seminars\/building-generative-ai-applications-the-tricks-and-tips-you-need-to-avoid-common-pitfalls\/","title":{"rendered":"Building Generative AI Applications: The tricks and tips you need to avoid common pitfalls"},"content":{"rendered":"

In this talk, you will learn about some of the common challenges that you might encounter while developing meaningful applications thta use generative AI models.<\/p>\n

Using non-deterministic systems as the basis for important applications is certainly an ‘interesting’ new frontier for software development, but hope is not lost. In this session, we will explore some of the well known (and less well known) issues in building applications on the APIs provided by Generative AI providers, like OpenAI or Anthropic, and on ‘open’ models, such as Mistral, Llama, or DeepSeek.<\/p>\n

We will also (of course) dive into some of the approaches that you can take to address these challenges, including agent\/MCP integration and graph-based memory, mitigating some of the inherent behaviors within Generative AI models, and enabling you to build more reliable and robust systems on top of Generative models, unlocking the potential of this new development paradigm.<\/p>\n

Takeaways<\/p>\n