In our last post, we perfected the search query. Now, we tackle a harder problem: teaching an AI to use it. This is the story of why our fully autonomous agent failed, and how we built a ‘Record and Refine’ workflow to make it truly powerful.

Building on our last discussion about RAG retrieval, this article tackles the query itself. We explore why a ‘one-size-fits-all’ boolean query fails and present a universal fallback strategy that ensures your AI can reliably search anywhere.

Discover the complexities of web search for RAG systems. This article explores common pitfalls, such as handling responsive design and e-commerce redirects, and provides robust solutions for reliable data retrieval.

Veridian.ai enhances website search with AI-powered capabilities, requiring no backend changes. Features include natural language understanding, smart product comparisons, and structured result displays.

Explore how AI-powered conversations are revolutionizing e-commerce, from the evolution of web interaction to practical implementation strategies for SMBs. Learn about the transition from static pages to intelligent conversational interfaces.

This tutorial offers a detailed workflow for optimizing Large Language Models (LLMs) for edge devices using Google Cloud Platform (GCP) and Hugging Face. It covers setting up a GCP environment, fine-tuning a small LLM, and then applying advanced optimization techniques including knowledge distillation, quantization (dynamic and static), and pruning. The guide also details how to export the final model to formats like ONNX and TFLite for deployment in browsers (with Transformers.js or ONNX Runtime Web) and on mobile devices.