Summary

Data and model moats are great ... if you have access to them.

The bad news: Data & model moats aren’t accessible to many product teams. Most teams don’t have proprietary training data lying around or a bunch of resources to pre-train or fine-tune their own models.

The good news: Product moats are the most defensible. You can build product moats by being thoughtful about how your team ships and iterates on AI products.

At Autoblocks, we fundamentally believe that the way your team operationalizes building with AI is more influential to your product’s success than underlying models.

This guide will give you an elegant mental model for crafting your team’s GenAI product-building process.

What's Inside?

• The GenAI Product Refinement Cycle
  • Configuration
  • Evaluation
  • Refinement
• Employing the Refinement Cycle