When you are building chat applications for internal teams or customer support, you need to isolate data to meet data residency and compliance requirements. Also, you want to chat with internal documents or APIs securely and safely. You can’t rely on public AI services like ChatG...

During new feature development, we frequently introduce new API versions to roll out new features gradually while keeping the existing API functional for the connected client applications. Oftentimes, the new API version has a new database schema change and we need to serve diffe...

Retrieval Augmented Generation (RAG) works by taking a user’s question, searching for information relevant to that question, and then including the retrieved information alongside the question in a prompt to an AI chat model ChatGPT/Claude/DeepSeek, please answer question X using...

As vector search and Retrieval Augmented Generation(RAG) become mainstream for Generative AI (GenAI) use cases, we’re looking ahead to what’s next. GenAI primarily operates in a one way direction, generating content based on input data. Generative Feedback Loops (GFL) are focused...

This blog explains how to build an AI powered email Assistant that can draft, edit, and send emails, store them as vector embeddings, and retrieve similar emails using contextual search. The assistant leverages Semantic Kernel for AI operations, Neon for vector storage, and Azure...

Neon SQL editor provides AI driven features, such as SQL generation to easily convert natural language requests to SQL. However, there may be situations where you need to build your own AI query assistant for custom needs. For example, you might need tighter integration with your...