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If you’ve spent any time building Retrieval-Augmented Generation (RAG) prototypes, you inevitably hit the exact same wall. You wire up a great embedding model, point it at an excellent local LLM, and
In an article I recently co-authored, we argued that a fundamental shift is underway in product design. The traditional principles of User Experience (UX), which rest on direct user control and manipulation, are becoming obsolete with the rise of tru...
For years, evaluating traditional machine learning models, while never simple, followed a well-trodden path. Your team knew the drill: assemble a labeled dataset, define success with metrics like precision and recall, and track performance. The core ...
For the past few years, the rise of large language models (LLMs) has fueled a growing industry of so-called "prompt experts"—people who claim to have mastered the art of crafting precise instructions to extract the best results from AI. But as LLMs b...
Machine learning engineers transitioning from experimental models to production systems can significantly benefit from adopting principles established in Site Reliability Engineering (SRE). By integrating SRE practices, ML engineers can build systems...
February 2 marks the first compliance deadline for the EU’s AI Act, the groundbreaking regulatory framework that officially took effect last August. This legislation sets a global precedent in defining clear boundaries for the development and deploym...
In the fast-paced world of digital advertising, it’s tempting to focus on the metric that’s easiest to measure: Click-Through Rate (CTR). After all, clicks provide immediate feedback, making it seem like a straightforward indicator of campaign perfor...
Given a query, instruct a generative model (ChatGPT) to write a passage to answer the question. The passage may contain factual errors, but it looks like a good answer! The generated passage is passed through an Encoder (Contriever) to get the embed...
In the Few-Shot Prompting approach, through a few demonstrations, generative models quickly adapt to a specific domain and learn to follow the task format. However, the few-shots examples are fixed for all test examples (during inference). This neces...
