Artificial intelligence concepts, tools, and real-world applications, from experimentation to practical integration.
Data quality is the hidden bottleneck in AI. Deduplication, normalization, validation, and handling missing or noisy values improve accuracy and stability. In practice, strong systems rely more on effective data cleaning than on bigger models.
Plugging data into an LLM doesn’t create value on its own. Models mirror data quality, so messy or inconsistent data leads to unreliable and misleading results. Because outputs can sound confident even when wrong, poor data creates hidden risks. Clean, structured data is essential for effective AI.
Modern AI like LLMs are powerful but specialized, not human-level intelligence. It performs well on defined tasks using learned patterns but lacks true understanding and autonomy. It is not AGI, so using it effectively requires recognizing its limits and relying on human judgment.
