Wiring OpenAI LLMs, programming n8n workflow triggers, configuring Pinecone vector databases, and building RAG chat tools.
Building programmatic node paths in n8n, handling webhook returns, parsing complex JSON structures, and wiring alert systems.
Implementing Retrieval-Augmented Generation (RAG) by embedding documents, storing in Pinecone, and serving context answers via GPT API.
Writing custom handlers to feed unstructured text into GPT models, formatting structured JSON outputs, and optimizing API token costs.
AI and automation bridges the gap between static databases and dynamic business intelligence. Rather than relying on simple scripts, modern developers build resilient workflow triggers that handle HTTP request fallbacks and alert Slack channels on error logs.
With the rise of agentic coding assistants, Model Context Protocol (MCP) has emerged as the standard way to expose local files, databases, and APIs securely to large language models. Exposing secure endpoints via custom node servers allows LLMs to interact with database records autonomously.