Common pitfalls when building generative AI applications
A guide to common mistakes developers make when building applications with generative AI, including overuse and poor UX integration.
Chip Huyen is a writer, computer scientist, and AI/ML systems expert focused on deploying machine learning in production. She is the author of bestselling books Designing Machine Learning Systems and AI Engineering, and has worked with NVIDIA, Snorkel AI, Netflix, and AI startups.
9 articles from this blog
A guide to common mistakes developers make when building applications with generative AI, including overuse and poor UX integration.
Explores AI agents, their capabilities, and frameworks for development, focusing on tools, planning, and evaluation.
Explores the common architectural components and implementation steps for building a scalable generative AI platform, from basic models to complex systems.
An analysis of 900 popular open-source AI tools, categorizing them into infrastructure, model development, and application layers.
Explores using predictive human preference to route AI prompts to the most suitable model, improving quality and reducing costs.
Explains key AI model generation parameters like temperature, top-k, and top-p, and how they control output creativity and consistency.
An in-depth exploration of Large Multimodal Models (LMMs), covering their fundamentals, key architectures like CLIP and Flamingo, and current research directions.
An overview of the top 10 open research challenges in Large Language Models (LLMs), focusing on reducing hallucinations and optimizing context learning.
A framework for developing a generative AI strategy, based on a talk exploring practical approaches for leaders and teams.