Reporting and perspectives from Chip Huyen. Headlines and excerpts link to the original articles.
Chip Huyen outlines six common pitfalls in building generative AI applications, drawing on public case studies and personal experience, including using gen AI when unnecessary, confusing bad product with bad AI, starting too complex, over-indexing on early success, forgoing human evaluation, and crowdsourcing use cases.
Chip Huyen published a blog post adapted from the Agents section of her book AI Engineering (2025). It covers how agents are defined, the role of tools in extending their capabilities, and the challenges of planning for complex tasks.
Chip Huyen outlines the common architecture of generative AI platforms, starting from a minimal query-to-model setup and progressively adding context construction, RAG retrieval approaches, guardrails for input and output, query rewriting, and agentic actions. The post describes components and considerations, not model evaluation, prompt engineering, finetuning, or RAG chunking strategies.
Chip Huyen describes three heuristics she uses to think about personal growth: rate of change, time to solve problems, and number of future options. She presents the piece as a thought exercise rather than a rigorous experiment, and notes some friends found the idea of measuring everything mildly sociopathic.
Chip Huyen published an analysis of 845 open source AI software repositories with at least 500 GitHub stars, examining the stack around foundation models. She reports 2023 saw the largest growth in application and application development layers, and that China's open source AI ecosystem has diverged from the Western one.
Chip Huyen explored predicting which model a user would prefer for a specific prompt, using LMSYS Chatbot Arena data. A DistilBERT-based preference predictor reached 76.2% accuracy on non-tie matches when given the prompt, versus 74.1% for Bradley-Terry ranking.
Chip Huyen published a long technical post explaining multimodality and large multimodal models (LMMs), covering why multimodal data matters, the different data modalities and multimodal tasks, and the fundamentals of systems like CLIP and Flamingo. It also surveys active LMM research areas including multimodal outputs and efficient training adapters.
Chip Huyen outlines ten open research directions in large language model work, based on conversations with people in industry and academia. She highlights hallucinations and context learning as the most discussed areas, and names multimodality, new architectures and GPU alternatives as the ones she is most excited about.
Chip Huyen prepared a talk titled "Leadership needs us to do generative AI. What do we do?" for Fully Connected, presenting a simple framework for exploring generative AI strategy. She says many ideas are still being fleshed out and she hopes to turn it into a proper post.