Conor Kelly's article explores Retrieval Augmented Generation (RAG) architectures, a technique enhancing large language models (LLMs) by integrating real-time data retrieval. The piece highlights how RAG overcomes limitations like hallucinations, ensuring factual and contextually relevant outputs. It details eight popular RAG architectures, ranging from the simple to more advanced approaches like Agentic RAG, each tailored for different use cases. These architectures offer varied workflows, including memory integration, branched data sourcing, hypothetical document embedding, adaptive strategies, and corrective mechanisms. The article emphasizes RAG's effectiveness in applications like customer support, research, and content creation, where real-time information and accuracy are crucial. The piece concludes by pointing to Humanloop as a tool for enterprises to develop and evaluate RAG-based AI applications.

This research introduces Graph RAG, a novel approach to enhance question answering over large text collections by combining knowledge graphs and retrieval-augmented generation (RAG). The method constructs a graph-based index from the text, identifies communities within the graph, and generates summaries for each community. Given a query, Graph RAG leverages these summaries to produce partial answers, which are then aggregated into a comprehensive global response. The study demonstrates that Graph RAG improves the comprehensiveness and diversity of answers compared to naive RAG, particularly for questions requiring a global understanding of the corpus. An LLM is used to evaluate the approach and a Python implementation will be available. The work also shows that the modular design of the approach is efficient in that the models need to process fewer tokens.

DeepSeek-AI introduces DeepSeek-R1, a reasoning model developed through reinforcement learning (RL) and distillation techniques. The research explores how large language models can develop reasoning skills, even without supervised fine-tuning, highlighting the self-evolution observed in DeepSeek-R1-Zero during RL training. DeepSeek-R1 addresses limitations of DeepSeek-R1-Zero, like readability, by incorporating cold-start data and multi-stage training. Results demonstrate DeepSeek-R1 achieving performance comparable to OpenAI models on reasoning tasks, and distillation proves effective in empowering smaller models with enhanced reasoning capabilities. The study also shares unsuccessful attempts with Process Reward Models (PRM) and Monte Carlo Tree Search (MCTS), providing valuable insights into the challenges of improving reasoning in LLMs. The open-sourcing of the models aims to support further research in this area.

Google's "AI Business Trends 2025" report analyzes key strategic trends expected to reshape businesses. It identifies five major trends including the rise of multimodal AI, the evolution of AI agents, the importance of assistive search, the need for seamless AI-powered customer experiences, and the increasing role of AI in security. The report suggests that companies need to understand how AI has impacted current market dynamics in order to innovate, compete and continue to evolve. Early adopters of AI are more likely to dominate the market and grow faster than traditional competitors. This analysis is based on data insights from studies, surveys of global decision makers, and Google Trends research, using tools like NotebookLM to identify these pivotal shifts.

This podcast presents a comprehensive survey of post-training techniques for Large Language Models (LLMs), focusing on methodologies that refine these models beyond their initial pre-training. The key post-training strategies explored include fine-tuning, reinforcement learning (RL), and test-time scaling, which are critical for improving reasoning, accuracy, and alignment with user intentions. It examines various RL techniques such as Proximal Policy Optimization (PPO), Direct Preference Optimization (DPO) and Group Relative Policy Optimization (GRPO) in LLMs. The survey also investigates benchmarks and evaluation methods for assessing LLM performance across different domains, discussing challenges such as catastrophic forgetting and reward hacking. The document concludes by outlining future research directions, emphasizing hybrid approaches that combine multiple optimization strategies for enhanced LLM capabilities and efficient deployment. The aim is to guide the optimization of LLMs for real-world applications by consolidating recent research and addressing remaining challenges.