Discover how Langfuse offers secure, open-source monitoring for LLM and GenAI solutions.

There’s been a lot of talk about GPT-3 and generative AI in the news, social media, and probably from every AI practitioner or vendor whom you’ve been speaking with lately. Everyone is super excited about the future that such AI tools hold. But what exactly is this AI technology specifically and what does it mean for your business and your AI problems? Let’s explore! What is GPT-3? What Can GPT-3 Do? The Business Benefits of GPT-3 Is traditional ML going away because of GPT-3? What are the risks of GPT-3?  GPT-3 Examples GPT-3 Key Takeaways Keep Learning & Succeed With AI Resources What is GPT-3? GPT-3 is a large language model developed by Open AI. It’s the successor of Open AI’s older language model, GPT-2 which was much smaller in comparison. So, what’s a language model? A language model is a probability distribution over sequences of words learned from data. This probability distribution can then be used to complete sentences, validate sentence correctness, validate speech recognition predictions, translate one language to another, and much more. As you can see, language models are pretty powerful, and in concept, this is not new. Language models have been around for decades. Here’s an example...

Deploying machine learning models can be daunting, particularly when considering the best environment to host your models. AWS and GCP offer robust cloud platforms, but the setup process varies significantly. Recently, we wrote a guide on deploying MLflow on Google Cloud Platform, and now we will share a comprehensive, step-by-step guide on setting up MLflow on AWS using Terraform. From setting up VPC to creating a database, ECS service and setting up security groups, we’ll walk you through the entire process modularly, with each section dedicated to a specific component. Give a read to understand how to create a robust, secure, and scalable MLflow setup on AWS, all while leveraging the power and convenience of Infrastructure as Code through Terraform. Repository For easy access and guidance, we’ve set up a repository containing all essential materials related to this guide. If you have any questions or run into problems, you can find answers there. AWS CLI Install AWS CLI: Follow this instruction on installing the AWS Command Line Interface (CLI). Generate your access key: Create a key for the user to manage your Terraform resources following this instruction. Login with AWS Configure: Use the AWS Configure command to log in. Terraform...

Conversational AI, powered by advanced technologies like natural language processing (NLP) and machine learning (ML), has revolutionized how businesses interact with customers. From chatbots and virtual assistants to voice-activated devices like Siri and Alexa, these systems offer automated, intelligent, and human-like conversations that enhance user experience and streamline operations. Recent studies show that AI chatbots now handle up to 85% of customer queries, with 90% of interactions expected to be managed by AI by 2027. While many customers prefer chatbots for quick answers, most still turn to humans for complex issues. This growing use of conversational AI highlights the need for quality data and ongoing improvements to maximize ROI and deliver smooth, natural conversations. This guide will help you understand the significance of high-quality data collection for conversational AI and share effective practices to ensure your AI solution delivers optimal business value. The Significance of Conversational AI As technology becomes more integrated into daily life, the way we interact with devices has evolved—from keyboards and touchscreens to voice commands. Conversational AI enables users to operate devices hands-free, issuing commands from a distance and receiving instant, personalized responses. This shift not only improves convenience but also opens new avenues for businesses...

While large reasoning models (LRMs) have shown impressive capabilities in short-context reasoning through reinforcement learning (RL), these gains do not generalize well to long-context scenarios. Applications such as multi-document QA, research synthesis, and legal or financial analysis require models to process and reason over sequences exceeding 100K tokens. However, RL optimization in such regimes is plagued by slower reward convergence, unstable policy updates due to KL divergence fluctuations, and reduced exploration resulting from entropy collapse. These bottlenecks reveal a fundamental gap in transitioning LRMs from short-context proficiency to long-context generalization. QwenLong-L1: A Structured RL Framework for Long-Context Adaptation To address these limitations, the Qwen Research team introduces QwenLong-L1, a novel RL framework designed to adapt LRMs to long-context reasoning tasks. The framework is structured into three key stages: Warm-up Supervised Fine-Tuning (SFT): Provides a stable initialization for the policy model by training on curated question-context-answer triplets, ensuring basic competence in contextual comprehension and answer extraction. Curriculum-Guided Phased Reinforcement Learning: Introduces a staged training process with gradually increasing context lengths. This progression enables the model to incrementally acquire long-context reasoning behaviors without destabilizing policy updates. Difficulty-Aware Retrospective Sampling: Enhances exploration by maintaining and reusing hard examples from previous phases, weighted by...

How many more coffees can our machine do before requiring maintenance? Well, that’s a fundamental question for most of the ELEDIA members. The obvious answer to this may be “we’ll know when it’s broken”. But if the machinery has a fundamental importance in the production line, will an unexpected downtime for maintenance be acceptable? Within the Maintenance, Repair and Overhaul (MRO) terminology, the previously outlined approach (substitution/maintenance of a machinery after the failure has taken place) would be referred to as a corrective maintenance, and it is known to be a suitable strategy only if the production process can be interrupted at any time with minimum consequences (btw, that’s NOT the case for the ELEDIA coffee machine). A completely opposite strategy is to schedule the maintenance after a pre-defined number of cycles / operation hours have been carried out, following the concept of preventive or planned maintenance. As the inspections are performed on a periodical basis, this strategy has the obvious advantage of guaranteeing fixed costs. But are such costs minimum? In the era of Smart Factories and Industry 4.0, the answer to such a question may be less obvious. Minimizing the maintenance costs by knowing in advance when exactly...

By Antti Innanen.Prompting is changing. We used to have elaborate prompts and ‘prompt engineers.’ And it used to be very useful. The first versions of AI tools needed quite a lot of guidance. If you had good, structured prompts, you got better results. But things are different now. The new models are much better at understanding user intent and natural language. These models think, meaning that ‘chain-of-thought’ reasoning is already happening inside the machine. You don’t need to tell it to go step-by-step, it’s doing that by default. And sometimes, structured prompts or bland instructions actually hurt performance. Enter the Vibe This shift has brought a new prompting idea into focus: vibe. The term comes from AI expert Andrej Karpathy. Instead of laying out detailed, step-by-step instructions, ‘vibing’ is about guiding the model through mood, tone, intent, and cultural references. You tell it how something should feel instead of spelling out how it should work. You trust the model to connect the dots. Talk Like a Human It sounds a bit odd, but it really works. AI tools see the world differently. Instructions that seem easy for humans can feel vague to an AI. Let me give you an example: I recently caught myself prompting with a...