diff --git a/package.json b/package.json
index e970d58..efd2def 100644
--- a/package.json
+++ b/package.json
@@ -1,6 +1,6 @@
 {
 	"name": "@ztimson/ai-utils",
-	"version": "1.2.12",
+	"version": "1.2.13",
 	"description": "AI Utility library",
 	"author": "Zak Timson",
 	"license": "MIT",
diff --git a/src/llm.ts b/src/llm.ts
index 776352c..e377387 100644
--- a/src/llm.ts
+++ b/src/llm.ts
@@ -397,15 +397,37 @@ class LLM {
 	 * @param {string} searchTerms Multiple search terms to check against target
 	 * @returns {{avg: number, max: number, similarities: number[]}} Similarity values 0-1: 0 = unique, 1 = identical
 	 */
-	fuzzyMatch(target: string, ...searchTerms: string[]) {
-		if(searchTerms.length < 2) throw new Error('Requires at least 2 strings to compare');
-		const vector = (text: string, dimensions: number = 10): number[] => {
-			return text.toLowerCase().split('').map((char, index) =>
-				(char.charCodeAt(0) * (index + 1)) % dimensions / dimensions).slice(0, dimensions);
-		}
-		const v = vector(target);
-		const similarities = searchTerms.map(t => vector(t)).map(refVector => this.cosineSimilarity(v, refVector));
-		return {avg: similarities.reduce((acc, s) => acc + s, 0) / similarities.length, max: Math.max(...similarities), similarities};
+	fuzzyMatch(target, ...searchTerms) {
+		if (searchTerms.length < 2) throw new Error('Requires at least 2 strings to compare');
+
+		const levenshtein = (a, b) => {
+			const m = a.length, n = b.length;
+			if (!m) return n;
+			if (!n) return m;
+			const dp = Array.from({length: m + 1}, (_, i) => [i, ...Array(n).fill(0)]);
+			for (let j = 0; j <= n; j++) dp[0][j] = j;
+			for (let i = 1; i <= m; i++) {
+				for (let j = 1; j <= n; j++) {
+					dp[i][j] = a[i - 1] === b[j - 1]
+						? dp[i - 1][j - 1]
+						: 1 + Math.min(dp[i - 1][j - 1], dp[i - 1][j], dp[i][j - 1]);
+				}
+			}
+			return dp[m][n];
+		};
+
+		const similarity = (a, b) => {
+			a = a.toLowerCase(); b = b.toLowerCase();
+			const dist = levenshtein(a, b);
+			return 1 - dist / Math.max(a.length, b.length, 1);
+		};
+
+		const similarities = searchTerms.map(t => similarity(target, t));
+		return {
+			avg: similarities.reduce((acc, s) => acc + s, 0) / similarities.length,
+			max: Math.max(...similarities),
+			similarities
+		};
 	}
 
 	/**