fix(curriculum): clarify DP function calls vs unique subproblems (freeCodeCamp#65749)

Author: Jeevankumar-s

curriculum/challenges/english/blocks/lecture-understanding-dynamic-programming/68420c7fb5b9e36eefb9e98f.md

@@ -46,7 +46,7 @@ This implementation has exponential time complexity because of massive redundant
 - `climb_stairs(2)` gets calculated **3 times total**
 
 
-For just `n=5`, we make 9 function calls when we only need 5 unique calculations. As `n` grows, this redundancy explodes exponentially - `climb_stairs(30)` would require over 1 billion function calls! The time complexity becomes `O(2^n)`, making it inefficient and impractical for larger values of `n`.
+For just `n=5`, we make 9 function calls when we only need 5 unique calculations. As `n` grows, this redundancy explodes exponentially - `climb_stairs(30)` would require a few million function calls. The time complexity becomes `O(2^n)`, making it inefficient and impractical for larger values of `n`.
 
 ## Dynamic Programming Solutions
 
@@ -113,7 +113,7 @@ Call: climb_stairs_memo(5)
 
 - **Space complexity**: `O(n)` for the memo storage and call stack
 
-- **Real impact**: `climb_stairs(30)` drops from 1+ billion calls to just 30 calls!
+- **Real impact**: `climb_stairs(30)` drops from millions of function calls to about 30 unique subproblem calculations
 
 
 ### Tabulation (Bottom-Up Approach)