diff --git a/content/ai/concepts/search-algorithms/terms/greedy-best-first-search/greedy-best-first-search.md b/content/ai/concepts/search-algorithms/terms/greedy-best-first-search/greedy-best-first-search.md
index b7a4724ec7a..010a5c04e5b 100644
--- a/content/ai/concepts/search-algorithms/terms/greedy-best-first-search/greedy-best-first-search.md
+++ b/content/ai/concepts/search-algorithms/terms/greedy-best-first-search/greedy-best-first-search.md
@@ -1,6 +1,6 @@
 ---
 Title: 'Greedy Best-First Search'
-Description: 'Greedy best-first seach is an informed search algorithm where the evaluation function is strictly equal to the heuristic function.'
+Description: 'Greedy best-first search is an informed search algorithm where the evaluation function is strictly equal to the heuristic function.'
 Subjects:
   - 'Data Science'
   - 'AI'
@@ -12,7 +12,9 @@ CatalogContent:
   - 'paths/machine-learning-ai-engineering-foundations'
 ---
 
-**Greedy best-first search** is an informed search algorithm where the evaluation function is strictly equal to the heuristic function, disregarding the edge weights in a weighted graph. To get from a start node to a target node, the lowest value resulting from some heuristic function, **h(x)**, is considered as the successive node to traverse to. The goal is to choose the quickest and shortest path to the target node.
+**Greedy best-first search** is an informed search algorithm where the evaluation function is strictly equal to the heuristic function, disregarding the edge weights in a weighted graph because only the heuristic value is considered. In order to search for a goal node it expands the node that is closest to the goal as determined by the heuristic function. This approach assumes that it is likely to lead to a solution quickly. However, the solution from a greedy best-first search may not be optimal since a shorter path may exist.
+
+In this algorithm, search cost is at a minimum since the solution is found without expanding a node that is not on the solution path. This algorithm is minimal, but not complete, since it can lead to a dead end. It's called "Greedy" because at each step it tries to get as close to the goal as it can.
 
 ## Evaluation Function