When you look at AI MVP development, the main takeaway is that most projects fail not because the technology isn’t powerful enough, but because teams try to build a “complete product” too early. In reality, the first step should always be narrowing the idea down to a single, testable use case — like document extraction, ticket classification, or a focused Q&A system over a specific dataset. Only then you can actually measure whether AI adds real value in a production-like environment.
Another common issue is underestimating data constraints. In many cases, data availability and quality matter more than the choice between models like GPT, Claude, or a custom pipeline. Without clean and relevant data, even the best model won’t deliver reliable results. That’s why architecture decisions (RAG vs. fine-tuning vs. rules-based logic) should come after validating the core hypothesis.
More details on the approach here:
