CMARIX Blog

Quick Overview: Chatbot AI never lies. Instead, it uses probabilities to generate its answers. In the absence of validation procedures such as RAG, validation checks, and output processes, chatbots may generate answers that sound confident but are untrue. This detailed guide on AI chatbot hallucinations explains more about why your AI chatbot is always lying...

Key Takeaways

Quick Summary: Choosing between vector databases and traditional databases is a critical decision for modern AI applications. While relational databases excel at structured, transactional data, they fall short at handling unstructured content such as text and images. Vector databases enable semantic search and support use cases such as RAGs and recommendatio...

Quick Summary: Most logistics tracking systems work fine in demos and fall apart in the field. This guide covers the architecture that actually holds up: offline queuing, MQTT transport, on-device AI inference, delta-encoded payloads, and dispatcher dashboards that render at scale. Whether you’re evaluating Flutter for a new build or rethinking an existing s...

Quick Summary: Enterprise RAG architecture allows AI systems to access and use internal company data in real time, solving the limitations of static models. By combining LLMs, retrieval, and embeddings, it delivers accurate, context-aware responses. When designed with proper data pipelines, evaluation, and security, RAG transforms scattered enterprise knowle...