Thoughts, articles, and resources on software development, SaaS, and digital growth.
The failure modes when AI is applied to raw, unfiltered data are consistent across tools and use cases. Here is what they look like in practice, and why they are a pipeline problem rather than a model problem.
Most AI research tools get the pipeline backwards. They apply language models to raw, unfiltered data and call the output intelligence. Here is why sequence matters more than model quality, and what a well-ordered pipeline looks like in practice.
Generative AI prioritises creativity over factuality. Research requires the opposite. Here is how source-anchored RAG with zero-temperature inference produces outputs that are traceable, repeatable, and legally defensible.