A personal study guide and experience report for the Microsoft AI-102 Azure AI Engineer Associate certification exam, covering preparation and key topics.
An infrastructure engineer explores AI Engineering, defining the role and its focus on using pre-trained models, prompt engineering, and practical application building.
A developer shares how they independently built two features for their personal AI system that were later released by Anthropic for Claude Code.
An engineer shares insights on how AI is transforming software development workflows and the rise of the AI-enhanced engineer.
An AI engineer explains how buying a printer for reading and annotating technical papers helps improve focus and retention.
A presentation on using Large Language Model (LLM) techniques to enhance Recommendation Systems (RecSys) and Search, from the AI Engineer World's Fair 2025.
Argues that effective AI product evaluation requires a scientific, process-driven approach, not just adding LLM-as-judge tools.
Notes on dataset engineering from Chip Huyen's 'AI Engineering', covering data curation, quality, coverage, quantity, and acquisition for AI models.
A summary of Chip Huyen's chapter on AI fine-tuning, arguing it's a last resort after prompt engineering and RAG, detailing its technical and organizational complexities.
Analysis of Chapter 6 from Chip Huyen's 'AI Engineering' book, focusing on RAG systems and AI agents, their architecture, costs, and relationship.
Analysis of Chip Huyen's chapter on AI system evaluation, covering evaluation-driven development, criteria, and practical implementation.
A summary and discussion of Chapter 1 of Chip Huyen's book, exploring the definition of AI Engineering, its distinction from ML, and the AI Engineering stack.
Explores AI agents, their capabilities, and frameworks for development, focusing on tools, planning, and evaluation.
A guide on transitioning into AI careers, distinguishing between working 'on' AI models and 'in' AI infrastructure, products, and engineering processes.
Reflections on delivering the closing keynote at the AI Engineer World's Fair 2024, sharing lessons from a year of building with LLMs.
Key takeaways from the AI Engineer Summit 2023, focusing on challenges in LLM deployment like evaluation methods and serving costs.
A summary of a keynote talk on essential building blocks for production LLM systems, covering evaluations, RAG, and guardrails.
