Evaluating LLM and AI agents Outputs with String Comparison, Criteria & Trajectory Approaches
Author(s): Michalzarnecki Originally published on Towards AI. When your model’s answers sound convincing, how do you prove they’re actually good? This article walks through three complementary evaluation strategies — string comparison, criteria-based scoring, and trajectory analysis. 1. String-Comparison Metrics Consider question below: …
LAI #77: Structured Outputs, LangGraph NLP, Sub-ms Agents, and Personalization at Scale
Author(s): Towards AI Editorial Team Originally published on Towards AI. Good morning, AI enthusiasts, This week’s issue is a mix of applied AI and infrastructure that actually scales. We start with a deep dive into structured output from both local and cloud-based …
ARGUS: Vision-Centric Reasoning with Grounded Chain-of-Thought
Author(s): Yash Thube Originally published on Towards AI. Existing Multimodal LLMs, primarily driven by advancements in large language models (LLMs), often underperform when accurate visual perception and understanding of specific regions-of-interest (RoIs) are crucial for successful reasoning. Argus tackles this by proposing …
The Essential Guide to ML Evaluation Metrics for Regression
Author(s): Ayo Akinkugbe Originally published on Towards AI. Photo by Europeana on Unsplash Introduction Machine learning models are only as good as our ability to measure them. Though a perfect model isn’t always possible, a good enough model is. But how do …
The Agent Course You Asked For Just Dropped — $99 Early Access
Author(s): Towards AI Editorial Team Originally published on Towards AI. Pay $99 for What Companies Pay $50K to Implement “Agent” has become one of the most overused — and underdefined — terms in AI. Sometimes it means “can call a tool.” Sometimes …
Why Ethics in AI Matters: Tackling Bias and Building Fair Machine Learning Systems
Author(s): Yuval Mehta Originally published on Towards AI. Photo by Christian Lue on Unsplash After learning that a test AI hiring tool discriminated against resumes that contained the word “women’s,” Amazon quietly discontinued it in 2018. The model had successfully taught itself …
LAI #78: RAG Evaluation, MCP 101, GRPO Fine-Tuning, and Multimodal Systems
Author(s): Towards AI Editorial Team Originally published on Towards AI. Good morning, AI enthusiasts, This week’s issue is for the builders who care about what works — and how to measure it. We’re starting with a deep dive into RAG evaluation pipelines: …
Fine-Tuning VLLMs for Document Understanding
Author(s): Eivind Kjosbakken Originally published on Towards AI. In this article, I discuss how you can fine-tune VLMs (visual large language models, often called VLLMs) like Qwen 2.5 VL 7B. I will introduce you to a dataset of handwritten digits, which the …
RAG in Practice: Exploring Versioning, Observability, and Evaluation in Production Systems
Author(s): Adil Said Originally published on Towards AI. I’ve seen a few posts on LinkedIn recently declaring RAG systems are dead. The core argument? “Context windows are getting bigger, so who needs retrieval anymore?” It got me thinking. RAG only really entered …
LAI #79: How LLMs Learn, Vertical Model Growth, and Smarter Evaluation
Author(s): Towards AI Editorial Team Originally published on Towards AI. Featured Good morning, AI enthusiasts, This week’s issue is about getting back to first principles. We’re diving into how LLMs actually learn: what’s under the hood, and why it matters when you’re …
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