How dltHub consulting partner Mooncoon speeds up complex dlt pipeline development 2x with Cursor, an analytics and data agency, leveraging AI-driven pipeline development methodology that integrates Large Language Models into the data pipeline development workflow through IDE extensions and specialized tools. This approach enables developers to focus on architectural decisions and data modeling while handling routine implementation details with AI assistants. The AI assistant can provide contextually aware recommendations for tasks such as boilerplate code, basic error handling, and schema definitions, improving productivity and reducing manual effort. By configuring the Cursor IDE properly, including setting up the LLM provider and context length, ignoring irrelevant files, designing a system prompt, and adding a knowledge base, developers can unlock the full potential of AI-assisted development for dlt pipeline development. The workflow involves maintaining a natural dialogue between developer and AI assistant, where the human guides the process while the AI handles routine implementation details, allowing for quick iteration cycles and experimentation with different approaches.