diff --git a/sqlite-lembed.c b/sqlite-lembed.c
index 479a554..9b9e574 100644
--- a/sqlite-lembed.c
+++ b/sqlite-lembed.c
@@ -57,23 +57,30 @@ int embed_single(struct llama_model *model, struct llama_context *context,
                  /** Output float embedding */
                  float **out_embedding,
                  /** Output embedding length (n dimensions) */
-                 int *out_dimensions) {
-  int n_batch = 512;
-  int n_ctx_train = llama_n_ctx_train(model);
+                 int *out_dimensions,
+                 /** Output error message (caller must sqlite3_free if not NULL) */
+                 char **out_error) {
   int n_ctx = llama_n_ctx(context);
 
   llama_token *tokens;
   int token_count;
   int rc = tokenize(model, input, input_length, &token_count, &tokens);
   if(rc != SQLITE_OK) {
-    // TODO error message
+    *out_error = sqlite3_mprintf("Failed to tokenize input");
     return rc;
   }
 
-  struct llama_batch batch = llama_batch_init(n_batch, 0, 1);
+  if (token_count > n_ctx) {
+    sqlite3_free(tokens);
+    *out_error = sqlite3_mprintf(
+        "Input too large: %d tokens exceeds model context size of %d",
+        token_count, n_ctx);
+    return SQLITE_ERROR;
+  }
+
+  struct llama_batch batch = llama_batch_init(token_count, 0, 1);
 
   int seq_id = 0;
-  // llama_batch_add(batch, tokens, 0, )
   for (int i = 0; i < token_count; i++) {
     batch.token[batch.n_tokens] = tokens[i];
     batch.pos[batch.n_tokens] = i;
@@ -85,6 +92,9 @@ int embed_single(struct llama_model *model, struct llama_context *context,
     batch.n_tokens++;
   }
 
+  sqlite3_free(tokens);
+  tokens = NULL;
+
   int dimensions = llama_n_embd(model);
   float *output_embedding = sqlite3_malloc(sizeof(float) * dimensions);
   if(!output_embedding) {
@@ -92,11 +102,12 @@ int embed_single(struct llama_model *model, struct llama_context *context,
     return SQLITE_NOMEM;
   }
 
-  llama_kv_cache_clear(context); // KV not needed for embeddings?
+  llama_kv_cache_clear(context);
   rc = llama_decode(context, batch);
   if(rc != 0) {
     sqlite3_free(output_embedding);
     llama_batch_free(batch);
+    *out_error = sqlite3_mprintf("Failed to decode input");
     return SQLITE_ERROR;
   }
 
@@ -110,6 +121,7 @@ int embed_single(struct llama_model *model, struct llama_context *context,
   if(!source_embedding) {
     sqlite3_free(output_embedding);
     llama_batch_free(batch);
+    *out_error = sqlite3_mprintf("Failed to extract embeddings");
     return SQLITE_ERROR;
   }
 
@@ -302,9 +314,15 @@ static void lembed(sqlite3_context *context, int argc, sqlite3_value **argv) {
 
   int dimensions;
   float *embedding;
-  rc = embed_single(model, ctx, input, input_len, &embedding, &dimensions);
+  char *error_msg = NULL;
+  rc = embed_single(model, ctx, input, input_len, &embedding, &dimensions, &error_msg);
   if(rc != SQLITE_OK) {
-    sqlite3_result_error(context, "Error generating embedding", -1);
+    if (error_msg) {
+      sqlite3_result_error(context, error_msg, -1);
+      sqlite3_free(error_msg);
+    } else {
+      sqlite3_result_error(context, "Error generating embedding", -1);
+    }
     return;
   }
   sqlite3_result_blob(context, embedding, sizeof(float) * dimensions, sqlite3_free);