Mat Velloso spent a decade building developer platforms at Meta, Google DeepMind, and Microsoft. Now he's joining Revelo as a strategic advisor. Here's why he thinks AI and engineering productivity are about to compound in ways most people haven't grokked yet.

RLHF is a crucial technique for improving the quality of code generated by LLMs. By incorporating human feedback, RLHF helps LLMs generate code that is more accurate, efficient, and aligned with human preferences. This article explores the benefits and challenges of RLHF and how it compares to alternative approaches. Learn how RLHF is shaping the future of AI-powered coding.

Large language models (LLMs) are revolutionizing code generation, but their performance can be significantly enhanced through post-training techniques. One crucial technique that gives LLMs an "unfair advantage" is incorporating human data.

This article delves into the exciting world of LLM-generated code and its predicted impact on software development in 2025. We explore key trends like increased accuracy, real-time feedback, and specialized LLMs for specific domains. Discover how human data is essential for refining LLMs through post-training techniques such as SFT, RLHF, and DPO, ensuring more reliable, ethical, and human-centric code generation.

This blog post explores the crucial role of diverse human data in refining Large Language Models (LLMs) during post-training. It highlights the benefits of incorporating multiple sources, such as domain expertise, varied language styles, and cultural diversity, to improve accuracy, generalization, and mitigate bias.

This comprehensive guide explores the crucial role of human data in LLM post-training. Learn about different data types, evaluation criteria, ethical considerations, and key factors to consider when choosing a provider for SFT, RLHF, and DPO to enhance your LLM's performance and ensure responsible AI development.

This article explores the benefits of using remote engineers based in Latin America for LLM post-training, focusing on code generation with SFT, RLHF, and DPO. Discover how Revelo provides access to a skilled and cost-effective talent pool to enhance LLM performance and ensure responsible AI development.

DPO is a cutting-edge technique that enhances the ability of LLMs to generate high-quality code. By directly optimizing model parameters based on human preferences, DPO offers a simpler and more efficient approach compared to traditional methods. This article explores the benefits and challenges of DPO and how it's shaping the future of AI-powered coding.

SFT is a powerful technique for refining large language models (LLMs) to generate high-quality code. By training LLMs on carefully curated datasets of code and human feedback, SFT improves accuracy, efficiency, and readability while reducing errors and enhancing security. This article explores the benefits and challenges of SFT, its role in responsible AI development, and how it compares to alternative approaches.