Maybe some devs here can help me, I was recently promoted to “head of AI” at my work despite being very outwardly ambivalent towards it. So I’m struggling to figure out what would actually create value instead of just being an expensive waste of time but still satisfy the higher ups AI lust.

My first idea that I thought would actually be useful was just setting up the architecture for an actual analytics database for us and then let them explore it with metabase (then letting them use Claude for their wow factor of exploring it with AI or whatever).

But now I’m somewhat at a loss, so any insight you all have would be really helpful!

  • MagicShel@programming.dev
    6·
    3 hours ago

    If you just vibe AI, it will lull you into thinking you’re doing great.

    1. Define what successful AI usage looks like and include metrics and measurements — include AI usage in your story/ticketing system.
    2. Define parameters for usage. Will it write documentation? Write tests? Write functions? Classes? Whole features? Will it review code? How are you making sure everyone understands the new code (i.e. you don’t want devs committing code they don’t understand or reviewers passing it)
    3. How will you reign in costs? I had 3 devs spend over $1500 (each) in thirty days and am in the process of a explaining this to my COO. Purchasing plans instead of using API keys gives you some natural boundaries for reigning in costs.
    4. Create standard processes. Don’t vibe. Have a standard for how your AI-facing documentation is structured. Have standards/templates for how prompts are structured. Have standard prompts with limited scope for specific tasks. For example have a prompt just for review that specifies to review from the standpoint of security and best practices.
    • saplyng@lemmy.worldOP
      1·
      2 hours ago

      Those are all very helpful points, especially the last one; I’ve always tried to include standardization in the company but was never in the position to enforce it before