Explores Microsoft's Graph RAG, an advanced RAG technique using knowledge graphs to answer global questions about datasets, with a hands-on setup guide.
Explores the causes and types of hallucinations in large language models, focusing on extrinsic hallucinations and how training data affects factual accuracy.
A detailed comparison of Anthropic's Claude 3 and the newer Claude 3.5 Sonnet AI models, covering performance, capabilities, and benchmarks.
Reflections on delivering the closing keynote at the AI Engineer World's Fair 2024, sharing lessons from a year of building with LLMs.
Explains the limitations of Large Language Models (LLMs) and introduces Retrieval Augmented Generation (RAG) as a solution for incorporating proprietary data.
The article argues for a shift from subscription-based online LLMs to offline-first Small Language Models (SLMs) due to privacy, security, and cost concerns.
Explores new research on instruction masking and LoRA finetuning techniques for improving large language models (LLMs).
Analyzes how LLMs and AI are making technical interviews harder, leading to more complex coding questions and increased cheating, and proposes work sample tests as a better alternative.
A summary of a talk on applying Large Language Models (LLMs) to build and deploy recommendation systems at scale, presented at Netflix's PRS workshop.
Analyzing if AI can replace humans using computational theory, comparing countable vs. uncountable problems and AI's inherent limitations.
Practical lessons from integrating LLMs into a product, focusing on prompt design pitfalls like over-specification and handling null responses.
A technical review of April 2024's major open LLM releases (Mixtral, Llama 3, Phi-3, OpenELM) and a comparison of DPO vs PPO for LLM alignment.
A review and comparison of the latest open LLMs (Mixtral, Llama 3, Phi-3, OpenELM) and a study on DPO vs. PPO for LLM alignment.
Argues against using LLMs to generate SQL queries for novel business questions, highlighting the importance of human analysts for precision.
A technical guide on using Azure AI Language Studio to summarize and optimize grounding documents for improving RAG-based AI solutions.
Practical tips for writing technical documentation that is optimized for LLM question-answering tools, improving developer experience.
A summary of February 2024 AI research, covering new open-source LLMs like OLMo and Gemma, and a study on small, fine-tuned models for text summarization.
A summary of key AI research papers from February 2024, focusing on new open-source LLMs, small fine-tuned models, and efficient fine-tuning techniques.
Explores the gap between generative AI's perceived quality in open-ended play and its practical effectiveness for specific, goal-oriented tasks.
A developer's critical reflection on GitHub Copilot's impact, questioning if its AI assistance is creating accessibility and quality divides in software development.
