Managing UI state for generative data requires understanding multi-stage, streamed, and interpretative outputs, contrasting with CRUD models. Key strategies include handling streaming updates, managing context as a first-class state, treating each stage as a state machine, and incorporating retries as a feature.

Building effective Retrieval-Augmented Generation (RAG) systems requires parsing user queries into structured parameters for precise retrieval and response generation. The Agentic RAG approach improves upon naive methods by understanding user intent and applying relevant filters, enhancing the quality of generated answers.

The rise of AI-native applications marks a pivotal shift in software design—where AI isn't just a feature, but the foundation. Yet, developers are held back by fragmented, AI-forced stacks that make building AI-native products complex, costly, and inefficient. In this blog, we explore the challenges of disconnected AI and product workflows, introduce the core components of AI-native software, and share how Unbody unifies these worlds into a seamless development stack to enable smarter, more adaptive applications.

Build a unique experience by mixing and matching components.

An invisible layer, a headless ai api, a seamless modular pipeline from private data to frontend

While AI dominated Web Summit 2023, the over-reliance on basic OpenAI integrations revealed a gap in the market. This post explores the untapped potential for more innovative AI applications and how platforms like Unbody are paving the way for deeper, more creative AI integration—without requiring extensive technical expertise.