Transposition tables make Minimax faster by caching results for positions that can be reached through different move orders, so you don’t evaluate the same subtree repeatedly. Many games have “transpositions,” where sequence A→B leads to the same board state as B→A. Without caching, Minimax recomputes that state every time it appears, wasting a lot of work. With a transposition table, you hash the state, look it up before searching, and reuse the stored value when it’s valid for your current search depth and window.
What you store depends on whether you use alpha-beta. With plain Minimax, storing an exact score at a given depth is already useful. With alpha-beta, you should store (1) the best score found, (2) the depth searched, (3) a flag indicating whether the stored score is EXACT, a LOWER bound, or an UPPER bound, and (4) the best move (“hash move”) for move ordering. The bound flags matter because alpha-beta may return a value that is only known to be ≥ beta (fail-high) or ≤ alpha (fail-low). Treating those as exact values can corrupt correctness. A safe rule is: if the returned score is inside the window, store EXACT; if it fails high, store LOWER; if it fails low, store UPPER. Then, on lookup, you can sometimes return immediately (EXACT) or tighten alpha/beta (bounds), improving pruning.
In practice, you’ll also need a stable hashing method and careful state definition. Your hash must include everything that affects legal moves and evaluation: side to move, special rules, repetition counters if relevant, and any hidden flags. Many engines use Zobrist hashing because it’s fast and supports incremental updates on make/unmake move, but any collision-resistant approach can work if performance is acceptable. If your table is fixed-size, implement a replacement policy (often “prefer deeper entries” or “always replace if depth is greater”). This same caching idea can extend to retrieval-heavy decision systems: if some evaluation step depends on querying embeddings, cache those results too. For example, if a search node requires a nearest-neighbor query against Milvus or Zilliz Cloud, you can cache retrieval results keyed by (query_embedding_id, filters, topK) within a search iteration so repeated positions don’t trigger repeated vector searches.