In a bold move, Klarna recently announced they are moving away from using well-established SaaS platforms like Salesforce and Workday. Instead, they are relying on an internal AI-driven solution to replicate — and, in some cases, surpass — decades of customization and workflow automation offered by these industry giants. This shift raises a critical question...
Author: Martin Treiber
Top Prompting Strategies Unveiled by Anthropic Experts
Prompt engineering is the bridge between human intention and AI output, and its impact on industries from healthcare to research is profound. As AI systems become more powerful, the ability to craft precise, effective prompts has emerged as a key skill in making these systems work for us. Anthropic’s prompt engineering experts have spent years...
What is the difference between prediction and recommendation?
Machine learning encompasses a range of techniques and methodologies designed to analyze data and make informed decisions. Two fundamental tasks within this field are prediction and recommendation. Understanding the distinction between these tasks is essential for effectively applying machine learning technologies across various domains. This article delves into the definitions, theoretical backgrounds, applications, ethical considerations,...
AI Simulates Classic DOOM
Imagine a world where you could play DOOM—yes, the iconic 1993 first-person shooter—powered not by a traditional game engine but by a neural network. Thanks to a groundbreaking new AI system called GameNGen, developed by researchers at Google Research, Google DeepMind, and Tel Aviv University, this is no longer a futuristic dream but a reality....
What is preference-driven refinement prompting?
Preference-driven refinement prompting is a technique used in AI prompt engineering to tailor the outputs of language models according to specific user preferences. This process involves iteratively refining prompts based on user feedback to achieve desired results. Here’s how it works: Initial Prompt Creation: Start with a basic prompt that outlines what you want the...
Is AI About to Make Developers Redundant?
Software developers, take note: your role might be about to evolve. That’s according to Matt Garman, head of Amazon Web Services (AWS), who recently suggested that AI could soon take over many programming tasks, reshaping the future of development. But before you panic and start brushing up on your résumé for a career change, there’s...
Why are separators important for prompt engineering?
Separators, also known as delimiters, play a crucial role in enhancing the performance and effectiveness of prompts used with Large Language Models (LLMs). The integration of separators within prompting is a strategy inspired by human cognitive processes, aimed at improving the reasoning capabilities of large language models (LLMs). This method, involves strategically placing separators in...
What is the difference between an AI bot and an AI agent?
The terms "AI bot" and "AI agent" are often used interchangeably, but there are some key differences between them. AI bots are typically designed to perform specific tasks or functions, such as responding to customer inquiries or providing information. They are often powered by machine learning and natural language processing, which allows them to learn...
What is Prompt Chaining?
Prompt chaining is a technique used in generative AI models, particularly within the realms of conversational AI and large language models (LLMs). This method involves using the output from one model interaction as the input for the next, creating a series of interconnected prompts that collectively address a complex problem or task[1][2]. This approach contrasts...
What is In-Context Learning of LLMs?
In-context learning (ICL) refers to a remarkable capability of large language models (LLMs) that allows these models to perform new tasks without any additional parameter fine-tuning. This learning approach leverages the pre-existing knowledge embedded within the model, which is activated through the use of task-specific prompts consisting of input-output pairs. Unlike traditional supervised learning that...
