Cursor Rules for Building RAG Pipelines

This is a free, MIT-licensed Cursor rules file for building retrieval-augmented generation pipelines the way AIML.chat does — it keeps your AI editor chunking and embedding sensibly, retrieving top-k, and grounding every answer in cited context. Drop it in .cursorrules for a RAG pipeline that doesn't hallucinate.

What these rules do

RAG quality lives in the details: chunks too big to be precise or too small to be coherent, retrieval that ignores relevance scores, prompts that let the model answer beyond the retrieved context. These rules teach your AI editor the patterns that make retrieval trustworthy — clean text before embedding, chunk with sensible size and overlap, retrieve top-k by cosine similarity, and assemble a prompt that instructs the model to answer only from the retrieved passages and cite them. They also push for handling the empty-retrieval case honestly instead of guessing. The result is a pipeline whose answers you can trust and trace.

The rules

# RAG pipeline (AIML.chat style)
- Clean and strip boilerplate before embedding.
- Chunk with sensible size and overlap; keep sections coherent.
- Retrieve top-k by cosine similarity; respect relevance scores.
- Assemble prompts that answer only from retrieved context, with citations.
- On weak or empty retrieval, say so and escalate — don't guess.
- Keep the embedding model and dimensions config-driven.

How to use it

Save the block as .cursorrules at the repo root of your pipeline project, and your AI assistant applies it. It pairs with the .NET agent rules for the surrounding code and the knowledge base rules for content structure. The documentation use case shows RAG in action.

Ready to build? Start free, or skip the pipeline and let AIML.chat handle RAG — see pricing for limits.