AI agents are programs that can perform tasks, make decisions, and even learn, all with a degree of autonomy. This guide provides a beginner-friendly introduction to the world of AI agents, explaining core concepts and walking you through building your first agent using Python. Table of Contents * Introduction * Basic AI
Developing applications powered by LLMs presents unique challenges: managing context, integrating external data, orchestrating multi-step reasoning, and ensuring scalability. This is where frameworks like AutoGen and LangChain come in. AutoGen (developed by Microsoft) and LangChain are two popular open-source frameworks designed to simplify the development of LLM-powered applications. While both
In today's AI-driven landscape, choosing between open-source and proprietary large language models (LLMs) represents a critical decision for businesses of all sizes. As organizations increasingly integrate AI into their operations, understanding the nuances of this choice can significantly impact everything from costs and performance to compliance and customization
Anthropic’s Claude 3.7 Sonnet and OpenAI’s O1 represent the latest advancements in AI reasoning, pushing the boundaries of mathematical logic, coding proficiency, and scientific analysis. While Claude 3.7 Sonnet boasts an adaptive reasoning approach optimized for real-world applications, OpenAI O1 is engineered for rigorous multi-step problem-solving
New LLMs emerge frequently, each boasting improvements in performance, cost-effectiveness, or specialized capabilities. As a prompt engineer, how can you harness the power of these advancements without being locked into a single model or constantly rewriting your prompts? The answer lies in model-agnostic prompts. Model-agnostic prompts are versatile instructions designed
Artificial intelligence has rapidly evolved, with large language models (LLMs) and other generative AI tools becoming increasingly accessible. But to truly harness the power of these models, from ChatGPT to image generators, you need to master the art of the prompt. This guide provides a comprehensive overview of prompt engineering,
On February 17th, xAI’s Grok 3 made its debut into the world, to much fanfare. In a livestream demo, it was revealed that Grok 3 is being deployed with a reasoning model. So, it’s natural to want to compare it with other, similar reasoning models such as OpenAI’
Prompt chaining is an AI technique that enhances the capabilities of large language models (LLMs). It involves breaking down a complex task into a series of interconnected prompts, where the output of one prompt becomes the input for the next. This structured approach guides the LLM through a more nuanced
Have you ever wondered how Large Language Models (LLMs) fine-tune their responses to be more creative or focused? The answer lies in understanding the "temperature" setting. This comprehensive guide will look into the intricacies of temperature in LLMs, exploring its impact, applications, and best practices. What is Temperature
Has OpenAI GPT-5 Been Released? As of February 21, 2025, OpenAI has not yet released GPT-5, but Sam Altman has confirmed that it is coming "in months, not weeks." While the exact release date remains unknown, OpenAI has outlined a clear roadmap for its next-generation AI systems. Before
"Evals are your company's intellectual property" - Alexander Bricken at AI Engineer Summit I recently attended Anthropic's talk at AI Engineer Summit, and it offered fascinating insights into how one of the leading AI companies thinks about building robust AI systems. Here are my
As of February 14, 2025, Anthropic has not officially announced the release of Claude 4. However, based on historical release patterns and industry speculation, it's anticipated that Claude 4 may debut in early to mid-2025. This projection aligns with Anthropic's iterative development approach, aiming to enhance
Large language models (LLMs) are only as good as the data they are trained on. Effective dataset management is crucial for improving model accuracy, efficiency, and adaptability. From curating high-quality datasets to versioning and optimizing prompts, robust dataset management tools play a key role in fine-tuning AI systems for better
OpenAI and DeepSeek are emerging as dominant players in the development of advanced language models, each bringing distinct strengths to the table. Their latest models, OpenAI's O1 and DeepSeek's R1, represent significant strides in AI reasoning and problem-solving. While OpenAI's O1 is engineered for
OpenAI's Model Spec tells us a lot about how the company thinks about prompt engineering. Let's explore it and see how to use it in your daily prompting. The Three-Layer Approach The Model Spec uses three layers: objectives, rules, and defaults. This structure makes prompts more
As AI models become increasingly sophisticated, there's a growing narrative that prompt engineering – the art and science of instructing large language models – will soon become obsolete. As models get better at understanding natural language, will the need for carefully crafted prompts will disappear? The death of prompt engineering
“More compute!” is a common refrain these days in discussions about enhanced LLM performance and capability. Just scan a few recent headlines to see the resources major companies are willing to pour into getting it. In a broad sense, this refers to more and better hardware used in model training.
One of the key factors driving the growth of Large Language Models (LLMs) is in-context learning (ICL), a unique learning paradigm that allows LLMs to adapt to new tasks by processing examples provided directly within the input prompt. This article breaks down the intricacies of ICL, exploring its mechanisms, benefits,
Large language models (LLMs) are revolutionizing how we interact with technology, but their complexity introduces unique challenges for developers. Ensuring LLMs perform reliably and efficiently requires robust observability—the ability to understand and diagnose their behavior. This article compares the best tools for measuring LLM observability, examining their key features
Software development is being fundamentally reshaped by AI, but the biggest challenge isn't technical expertise – it's domain knowledge. The next generation of AI products will be built by doctors, lawyers, and educators, not just machine learning engineers. We're excited to announce that PromptLayer has
As AI systems become more sophisticated and integrated into our daily lives, the need for robust, reliable, and comprehensive evaluation tools becomes increasingly critical. AI evaluation tools enable developers, researchers, and organizations to assess the performance, identify weaknesses, and ensure the ethical and responsible deployment of AI models. This article
While both DeepSeek V2 and Coder V2 leverage DeepSeek's innovative Mixture-of-Experts (MoE) architecture, DeepSeek V2 is a versatile, general-purpose language model excelling in both natural language processing and code generation, whereas Coder V2 is specifically designed and optimized for a wide array of coding tasks Table of Contents
OpenAI's upcoming o3 and DeepSeek's r1 represent significant advancements in the domain of reasoning models. Both models have garnered attention for their impressive performance on various benchmarks, sparking discussions about the future of AI and its potential impact on various industries. From what we know, OpenAI&
DeepSeek, a new Chinese AI company that released r1, is turning heads and this is a comprehensive blog to learn about its implications. Founded in late 2023 by Liang Wenfeng—a serial entrepreneur who also runs the hedge fund High-Flyer—DeepSeek is now a major AI player. Its models are