LLM
AI Guardrails and Trustworthy LLM Evaluation: Building Responsible AI Systems
Introduction: The Rising Need for AI Guardrails As large language models (LLMs) grow in capability and deployment scale, the risk of unintended behavior, hallucinations, and harmful outputs increases. The recent surge in real-world AI integrations across healthcare, finance, education, and defense sectors amplifies the demand for robust safety mechanisms. AI guardrails—technical and procedural controls ensuring…
Getting Started with Mirascope: Removing Semantic Duplicates using an LLM
Mirascope is a powerful and user-friendly library that provides a unified interface for working with a wide range of Large Language Model (LLM) providers, including OpenAI, Anthropic, Mistral, Google (Gemini and Vertex AI), Groq, Cohere, LiteLLM, Azure AI, and Amazon Bedrock. It simplifies everything from text generation and structured data extraction to building complex AI-powered…
Context Rot: How Increasing Input Tokens Impacts LLM Performance
Recent developments in LLMs show a trend toward longer context windows, with the input token count of the latest models reaching the millions. Because these models achieve near-perfect scores on widely adopted benchmarks like Needle in a Haystack (NIAH) [1], it’s often assumed that their performance is uniform across long-context tasks. However, NIAH is fundamentally…
Getting Started with MLFlow for LLM Evaluation
MLflow is a powerful open-source platform for managing the machine learning lifecycle. While it’s traditionally used for tracking model experiments, logging parameters, and managing deployments, MLflow has recently introduced support for evaluating Large Language Models (LLMs). In this tutorial, we explore how to use MLflow to evaluate the performance of an LLM—in our case, Google’s…
IBM sees enterprise customers are using ‘everything’ when it comes to AI, the challenge is matching the LLM to the right use case
Join the event trusted by enterprise leaders for nearly two decades. VB Transform brings together the people building real enterprise AI strategy. Learn more Over the last 100 years, IBM has seen many different tech trends rise and fall. What tends to win out are technologies where there is choice. At VB Transform 2025 today, Armand Ruiz, VP…
ether0: A 24B LLM Trained with Reinforcement Learning RL for Advanced Chemical Reasoning Tasks
LLMs primarily enhance accuracy through scaling pre-training data and computing resources. However, the attention has shifted towards alternate scaling due to finite data availability. This includes test-time training and inference compute scaling. Reasoning models enhance performance by emitting thought processes before answers, initially through CoT prompting. Recently, reinforcement learning (RL) post-training has been used. Scientific…
AI Acts Differently When It Knows It’s Being Tested, Research Finds
Echoing the 2015 ‘Dieselgate’ scandal, new research suggests that AI language models such as GPT-4, Claude, and Gemini may change their behavior during tests, sometimes acting ‘safer’ for the test than they would in real-world use. If LLMs habitually adjust their behavior under scrutiny, safety audits could end up certifying systems that behave very differently…
Fine-tuning vs. in-context learning: New research guides better LLM customization for real-world tasks
Join our daily and weekly newsletters for the latest updates and exclusive content on industry-leading AI coverage. Learn More Two popular approaches for customizing large language models (LLMs) for downstream tasks are fine-tuning and in-context learning (ICL). In a recent study, researchers at Google DeepMind and Stanford University explored the generalization capabilities of these two…
A Coding Guide to Asynchronous Web Data Extraction Using Crawl4AI: An Open-Source Web Crawling and Scraping Toolkit Designed for LLM Workflows
In this tutorial, we demonstrate how to harness Crawl4AI, a modern, Python‑based web crawling toolkit, to extract structured data from web pages directly within Google Colab. Leveraging the power of asyncio for asynchronous I/O, httpx for HTTP requests, and Crawl4AI’s built‑in AsyncHTTPCrawlerStrategy, we bypass the overhead of headless browsers while still parsing complex HTML via…
Salesforce AI Released APIGen-MT and xLAM-2-fc-r Model Series: Advancing Multi-Turn Agent Training with Verified Data Pipelines and Scalable LLM Architectures
AI agents quickly become core components in handling complex human interactions, particularly in business environments where conversations span multiple turns and involve task execution, information extraction, and adherence to specific procedural rules. Unlike traditional chatbots that handle single-turn questions, these agents must hold context over several dialogue exchanges while integrating external data and tool usage….
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