Quality Engineering

Hosting a Large Language Model Locally for QA Tasks

Introduction The biggest nightmare of a QA when it comes to using an AI for client deliverables is data leak. As we are aware, Once data is shared with many public generative AI models—especially those on third-party platforms—it may be stored and used for future training, posing data privacy and compliance risks, unless the user […]

Data Engineering

Using AI to build my first Android app: What worked and what didn’t

Initial Thoughts Having spent over a decade working in software development and data engineering, I thought, where is AI right now? Is it capable of eliminating the developer or is there still some time? So, I challenged myself with building an Android app. That was an unfamiliar area for me. While it intrigued me, there […]

.NETJava/JVM

Step-by-Step Guide to Implementing RAG with Spring Boot and PostgreSQL

Introduction Generative AI (Gen AI) has revolutionized how machines generate text, code, images, and more by leveraging deep learning models. However, one of its key limitations is its reliance on pre-trained knowledge, which may become outdated or lack domain-specific insights. This is where Retrieval-Augmented Generation(RAG) comes into play. RAG enhances Gen AI models capabilities by […]

Drupal

AI in Drupal: Building Real-World Applications with LLMs

Introduction Artificial Intelligence (AI) is revolutionizing web development by increasing efficiency and driving productivity. Integrating AI-driven Large Language Models (LLMs), like ChatGPT, with Drupal allows organizations to enhance user experiences, streamline processes, and promote innovation. However, merely connecting an LLM API is not sufficient—it is essential to create applications that deliver real value. In this […]