Step 1 (1 minute): Create a document index (collection of documents which will be queried together at run-time), upload documents either through our API endpoint or our UI.

Step 2 (2 minutes): Once the documents are indexed using your chosen embedding model and chunking strategy, they are stored in a vector database and can be queried through our search API. Choose the number of chunks you want returned.

Step 3 (5 minutes): Go to Vellum Playground, start with our predefined prompt templates, do some prompt engineering, add the relevant chunks to your test cases and confirm the LLM is providing reasonable results.