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Introducing the guide to AI-assisted engineering

A field guide for engineering leaders and developers to get more out of AI code assistants

Today we’re releasing the “Guide to AI-Assisted Engineering," a research-backed handbook to help engineering organizations effectively implement AI code assistants and achieve measurable productivity gains. This guide addresses a critical industry challenge: while organizations are rapidly investing in AI coding tools, many developers lack clear guidance on how to integrate these technologies into their workflows.

The guide was developed based on research and interviews with engineers from multiple organizations who report saving more than one hour per week using AI assistants, as well as from experienced AI users. It provides strategies for individual developers to leverage the full potential of these tools, as well as recommendations for leaders encouraging AI use.

“Rolling out AI code assistants is becoming a key priority for many technology organizations,” notes Justin Reock, Deputy CTO of DX and lead author of the guide. “Despite significant enthusiasm around these tools, achieving widespread developer adoption and optimal usage remains challenging.”

The guide is structured to address this challenge from multiple angles:

  • Effective AI prompting techniques - Practices used by experienced AI users, including meta-prompting, prompt-chaining, one-shot/few-shot learning, and deterministic engineering
  • Top developer-recommended use cases - The ten most valuable use cases for AI assistants, ranked by self-reported time savings
  • Leadership strategies - Approaches for engineering leaders to drive successful AI adoption within their organizations

Research partners for this guide include senior leaders from Sparksoft, Intagere, Odevo, IT Revolution, TinyMCE, and Fiserv, alongside dozens of anonymous participants.

This guide serves as a “zero-to-one” resource that helps teams get started quickly with AI tools while enabling organizations to discover additional use cases specific to their context. Engineering leaders can distribute this guide directly to their teams to enable and encourage AI use.

For more information about the guide and how to implement its recommendations, download the full document today.