Technical Blogs and Discussion Board

AI Observability: You Can’t Improve What You Can’t See

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Why monitoring AI applications in production requires more than checking whether the application is running Introduction: The Demo Worked. What Happens Next? Building an AI application has become easier. Developers can use large language models (LLMs), retrieval-augmented generation (RAG), APIs, and AI-assisted coding tools to create useful applications faster than ever. But getting an AI…

From Chess Player to Website Creator: How My Son Built ChessLens with the Help of AI

How a passion for chess, software development, and AI-assisted coding came together to create a live chess analysis platform. Sometimes, the most interesting technology projects begin with a personal passion. In our family, chess has always been more than just a game for my son. He is a chess player, and his interest in…

Vector Databases Explained: How AI Finds Meaning in Data

Subtitle: Understanding embeddings, similarity search, metadata filtering, and the architecture behind modern AI applications. When building an AI application, one of the first challenges is finding the right information from a large collection of documents. Imagine an engineering organisation with thousands of technical manuals, design reports, research papers, and troubleshooting records. A user asks: “How…

RAG Explained: How to Give AI Access to Your Own Knowledge

Subtitle: Understanding Retrieval-Augmented Generation, from document processing and vector search to reliable, source-grounded answers. One of the common challenges when building an AI application is helping it answer questions about information that is specific to a company, project, or technical domain. A general-purpose language model may understand programming concepts, software architecture, and engineering terminology. But…

AI and Data Privacy: Building Intelligent Systems Without Losing Control of Your Data

Subtitle: Practical architecture principles for protecting business information in AI-powered applications. Artificial intelligence becomes much more useful when it can work with real business information. An AI assistant connected to technical documentation can help engineers find answers faster. A system that understands company procedures can assist employees with routine questions. An application connected to business…

Evaluating AI Applications: From Impressive Demos to Measurable Results

Subtitle: A practical engineering approach to testing, benchmarking, and improving AI-powered software. An AI application can look impressive during a demonstration. It may answer questions fluently, summarize a document in seconds, or generate a useful piece of code from a short instruction. But a successful demonstration does not necessarily mean the application is ready for…