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Cursor AI Done Right: Lessons from Building Multiple MVPs

Cursor is really dumb if not given enough context about your project. Here what you can do to improve your Cursor workflow

1. Brainstorm first, code second

Claude/o1 are your best friends here. You should create a whole document containing every single detail of your project.

• core features
• goals & objectives
• tech stack & packages
• project folder structure
• database design
• landing page components
• color palette
• copywriting

All this should be put into an instruction.md (name it however you want) so Cursor can index at any time.

What's Model Context Protocol (MCP)

Introduction

Model Context Protocol (MCP) is an open protocol that standardizes how applications provide context to LLMs. It's a new standard for connecting AI assistants to the systems where data lives, including content repositories, business tools, and development environments. Its aim is to help frontier models produce better, more relevant responses.

Why MCP?

As AI assistants gain mainstream adoption, the industry has invested heavily in model capabilities, achieving rapid advances in reasoning and quality. Yet even the most sophisticated models are constrained by their isolation from data—trapped behind information silos and legacy systems. Every new data source requires its own custom implementation, making truly connected systems difficult to scale.

MCP addresses this challenge. It provides a universal, open standard for connecting AI systems with data sources, replacing fragmented integrations with a single protocol. The result is a simpler, more reliable way to give AI systems access to the data they need. ​

What is RAG?

Introduction🚀

RAG is a popular method that improves accuracy and relevance by finding the right information from reliable sources and transforming it into useful answers.

Large Language Models are trained on a fixed dataset, which limits their ability to handle private or recent information. They can sometimes "hallucinate", providing incorrect yet believable answers. Fine-tuning can help but it is expensive and not ideal for retraining again and again on new data. The Retrieval-Augmented Generation (RAG) framework addresses this issue by using external documents to improve the LLM's responses through in-context learning. RAG ensures that the information provided by the LLM is not only contextually relevant but also accurate and up-to-date.

There are four main components in RAG: