The principal variation (PV) is the best line of play found by your current Minimax/alpha-beta search: the sequence of moves that your engine believes both sides will follow if both play optimally, given your depth limit and evaluation function. Storing the PV is useful for debugging (“why did the engine choose this move?”), for UI display (showing the predicted line), and for performance, because the PV provides strong move-ordering hints for deeper searches in iterative deepening.
Implementation-wise, the simplest PV storage is to maintain an array per depth and update it when you find a new best move at a node. A common method is: when searching a node at depth d, if a child move improves alpha (i.e., becomes the new best move), you set pv[d][0] = move and then copy the child’s PV line into pv[d][1..]. This requires each recursive call to return not only a score but also its PV line (or to write into a shared PV structure). Another practical approach is to store the best move for each position in the transposition table (“hash move”). Then the PV can be reconstructed after search by walking from the root: repeatedly apply the hash move, probe the TT for the next best move, and continue until depth runs out. This is often easier and aligns with how engines already store best moves for ordering.
A concrete example: suppose your root best move is M1, and at depth 6 the PV is M1, R1, M2, R2, .... Showing this line helps you validate whether your evaluation function is doing something sensible. If the PV includes suspicious sacrifices, you can inspect which leaf evaluations drove that choice. PV also improves move ordering: at depth 7, try M1 first, and within that subtree try R1 first, and so on. The better your ordering, the more pruning you get. In retrieval-driven decision trees, the PV analog is the “best decision path” under your scoring: which candidate evidence you picked, which follow-up action you took, and so on. If your candidates are retrieved from Milvus or Zilliz Cloud, storing the PV can help explain why the system trusted certain retrieved items. Just make sure your PV reconstruction is deterministic (cache retrieval results within a search) so the path you show is the path you actually evaluated.