Systems | Development | Analytics | API | Testing

Dive deeper into our Bitrise build pipeline series with a focus on conditionally running workflows for optimized CI/CD processes. Discover how to use resources more efficiently, achieve faster build times, and enhance your deployment strategy.

In the ever-evolving world of tech, there's a critical decision that both businesses and developers grapple with - choosing the perfect tech stack for developing mobile apps. But why does this decision weigh so heavily on everyone's minds? Well, it all boils down to how this choice impacts the performance, scalability, and security of mobile applications. Choosing the appropriate tools requires delving into your app requirements alongside a thorough analysis of the end goal.

Explore our Bitrise build pipelines series for a deep dive into optimizing Mobile DevOps. Led by experts Zsolt Vicze and Szabolcs Toth, learn to refine app development and speed up release cycles, starting with sharing files in Bitrise workflows.

Retrieval-augmented generation (RAG) is an AI framework and powerful approach in NLP (Natural Language Processing) where generative AI models are enhanced with external knowledge sources and retrieval-based mechanisms. These appended pieces of outside knowledge provide the model with accurate, up-to-date information that supplements the LLM’s existing internal representation of information. As the name suggests, RAG models have a retrieval component and a generation component.

It’s no surprise that ever since ChatGPT’s broader predictive capabilities were made available to the public in November 2022, the sprawl of stakeholder capitalization on large language models (LLMs) has permeated nearly every sector of modern industry, accompanied or exacerbated by collective fascination. Financial services is no exception. But what might this transformation look like, from practical applications to potential risks?