Chess engines work by evaluating millions of positions per second using sophisticated algorithms. Traditional engines like Stockfish use alpha-beta search with handcrafted evaluation functions, while neural network engines like Leela Chess Zero (Lc0) use machine learning trained on millions of games. Both approaches achieve playing strength far beyond human capability.
Engines have transformed chess preparation and analysis. Players at all levels use engines to analyze their games, identify mistakes, and understand complex positions. Professional players rely heavily on engine-assisted preparation to find novelties and evaluate opening lines. The engine evaluation bar showing who stands better has become a ubiquitous feature of chess broadcasts.
While engines are powerful tools, using them effectively requires chess understanding. An engine can tell you the best move, but understanding why it is best requires human interpretation. The most productive way to use engines for improvement is to analyze your games, form your own assessment of each position, then compare your thinking with the engine's suggestions.