Category Archives: Deep Learning

Epochs: Maximizing Model Performance

  In our ever-evolving journey toward building smarter, more reliable AI systems, one concept has proven indispensable: epochs. At its core, an epoch represents one complete pass over the entire training dataset—a cycle in which our model learns from every available example. As a team that thrives on innovation and continuous improvement, we’ve developed strategies Continue Reading »

Basically a Made-up Language

Revolutionizing AI Development: Our Journey with BAML

  Over the past year, our team has witnessed a seismic shift in how we approach AI application development. One of the most exciting innovations we’ve embraced is BAML—a domain-specific language designed specifically for structured prompt engineering. In our journey, BAML has not only simplified our workflow but has also revolutionized the way we create Continue Reading »

Supervised Fine-Tuning (SFT) – Enhancing Model Performance

Supervised Fine-Tuning (SFT) – Enhancing Model Performance

Supervised Fine Tuning (SFT) – Improving Models for Particular Scenarios The painstaking process that is the evolution of Artificial Intelligence (AI) has yielded exceptionally complex models capable of a variety of tasks, each performed with astounding efficiency. Unfortunately, these models often lack one crucial element: versatility. This is where Supervised Fine Tuning (SFT) proves to Continue Reading »

AI Agents

AI Agents: The Future of Autonomous Decision-Making

The world of artificial intelligence is rapidly evolving, and at the forefront of this revolution is the concept of AI agents. These autonomous entities are designed to perform tasks, make decisions, and solve problems independently, often in real-time, with minimal human intervention. As AI agents become more sophisticated, they transform industries by automating complex processes, Continue Reading »