Why your roadmap's AI feature might be a compliance nightmare
When I look at product roadmaps today, I see so many "add AI" checkboxes that feel more like decorations than actual solutions. If you are just slapping an LLM onto a feature because it looks trendy, you are likely setting yourself up for a headache. The real magic happens when you stop asking "how can we use an LLM?" and start asking "where is our data being mishandled or stuck?"
I’ve seen much more success when we focus on the heavy lifting that actually helps people: taking those messy, unstructured documents that drive us analysts crazy and turning them into structured, clean data. Or helping a user get past that terrifying blank page syndrome by drafting an initial outline. These are practical, high-value wins.
But please, for the sake of your budget and your security team, don't over-engineer! If a standard PostgreSQL query or a simple regex can do the job, use it. There is no reason to send a deterministic task—like validating an email format—to a massive, expensive model. It’s slow, it’s costly, and it’s unpredictable.
If you truly need the model to understand your specific context, stick to RAG (Retrieval-Augmented Generation). Using something like pgvector is such a lifesaver because it keeps the answers grounded in your actual data, which is much safer for compliance than jumping straight into fine-tuning. Fine-tuning should almost always be your absolute last resort; it's so rigid and expensive!
The most impressive thing I see from product leads lately isn't their ability to implement the newest model, but their discipline in knowing exactly when to leave the AI out of it entirely.
https://github.com/pgvector/pgvector