Chain of Thought (CoT) is a prompting technique introduced in a 2022 paper by Google researchers (Wei et al., “Chain-of-Thought Prompting Elicits Reasoning in Large Language Models”). The core idea is simple: instead of asking an LLM for a direct answer, you instruct it to reason step by step. This elicits better performance on tasks requiring logic, math, commonsense, or multi-step planning.

Source: Internet

For example:

• Direct Prompt: “What is 15% of 200?”
• CoT Prompt: “What is 15% of 200? Let’s think step by step.”

The LLM might respond:

• “Step 1: 15% means 15 per 100, so 15/100 = 0.15.
• Step 2: Multiply by 200: 0.15 * 200 = 30. So, the answer is 30.”

ReAct, short for Reasoning and Acting, is a paradigm for enhancing large language models (LLMs) by integrating verbal reasoning traces with task-specific actions. Introduced in a 2022 paper, it addresses limitations in chain-of-thought (CoT) prompting by allowing models to interact with external environments, such as APIs or databases, to gather real-time data. This makes LLMs more reliable for tasks requiring factual accuracy or multi-step planning.

In the evolving field of artificial intelligence, large language models (LLMs) have transformed how we approach problem-solving, but they often struggle with hallucinations—generating plausible but incorrect information—or handling tasks requiring real-world interaction. Enter ReAct (Reasoning and Acting), a prompting framework that synergizes reasoning traces with actionable steps, enabling LLMs to behave more like intelligent agents. This detailed blog explores ReAct’s foundations, mechanics, advantages, and practical implementation, culminating in a sample Python application using LangChain. We’ll draw on established research and code examples to provide a comprehensive guide, updated with insights as of 2025.

How ReAct Works

In ReAct, the LLM generates a “thought” to plan, selects an “action” from available tools, observes the outcome, and iterates. This loop continues until the model outputs a final answer. For example, answering “What is Olivia Wilde’s boyfriend’s age raised to the 0.23 power?” might involve searching for the boyfriend, then calculating the power.

Source: Internet

Key Points

• ReAct Framework: It seems likely that ReAct is a prompting technique enabling LLMs to alternate between reasoning (thinking step-by-step) and acting (using tools like searches or calculations), improving accuracy on complex tasks by reducing hallucinations and incorporating external information.
• Core Process: Evidence leans toward a loop of Thought (reasoning), Action (tool invocation), Observation (results), repeating until a final answer, mimicking human problem-solving.
• Benefits and Limitations: Research suggests ReAct enhances interpretability and performance on knowledge-intensive and decision-making tasks, though it may increase computational costs and rely on well-defined tools; it’s particularly useful for dynamic environments but less so for simple queries.

RAG combines retrieval from external sources with LLM generation to produce informed responses. For instance, it retrieves documents from a vector store before prompting the model.

MCP, introduced by Anthropic, acts as a “USB-C for AI,” allowing models to dynamically access tools and data via a client-server model. It supports prompts, resources, and tools for contextual enhancement.

A2A, developed by Google, enables agents to exchange tasks and results over HTTP, using Agent Cards for discovery. It’s modality-agnostic, supporting text, images, and more.

Related terms include ReAct (reasoning + acting loop for decision-making) and ACP (local-first agent coordination, differing from A2A’s web-native focus).

Source: Internet

In the era of large language models (LLMs) like GPT-3 and Llama, fine-tuning these behemoths for specific tasks has become a cornerstone of AI development. However, traditional full fine-tuning demands enormous computational resources, often requiring hundreds of GBs of GPU memory and extensive training time. This is where parameter-efficient fine-tuning (PEFT) techniques shine, allowing us to adapt massive models with minimal overhead. Among these, Low-Rank Adaptation (LoRA) and its quantized variant, Quantized LoRA (QLoRA), stand out for their efficiency and effectiveness. In this technical blog, we’ll explore the mechanics, mathematics, advantages, and practical implementations of LoRA and QLoRA, drawing from foundational research and real-world applications.

Understanding Fine-Tuning Challenges

Full fine-tuning involves updating all parameters of a pre-trained model on a downstream dataset, which maximizes performance but at a steep cost. For instance, fine-tuning a 175B-parameter model like GPT-3 requires retraining every weight, leading to high memory usage and deployment challenges. PEFT methods mitigate this by updating only a subset of parameters or adding lightweight adapters, reducing trainable parameters by orders of magnitude while preserving model quality.

Source: Internet

Data centers are the backbone of the digital economy, housing the servers, storage systems, and networking equipment that power cloud computing, web services, and data-intensive applications. In the United States, data centers are strategically located to meet the demands of businesses, governments, and consumers. The rise of artificial intelligence (AI) has further amplified the importance of data centers, requiring specialized infrastructure to handle complex computational workloads. This article explores the primary locations of data centers in the US, the reasons behind their selection, and recent developments driven by AI.

Major Data Center Locations in the United States

The US hosts approximately 5,381 data centers, with significant concentrations in specific regions that offer optimal conditions for operation. The top data center markets include: