diff --git a/tests/testing.PY b/tests/testing.PY
new file mode 100755
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+++ b/tests/testing.PY
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+#! /Library/Frameworks/Python.framework/Versions/3.12/bin/python3
+# describe.py
+# To fulfill the test of asking the LLMs to describe
+
+# Import modules.
+import ollama;
+import json;
+import os;
+import datetime;
+
+# Add source files.
+IMPORTED = {'Strings': "data/datasets/strings.JSON", 'Prompts': "tests/config/prompts.json", 'Models': 'tests/config/models.JSON'}
+
+# Set up the main variables.
+RESPONSES = {};
+PROMPTS = {};
+RESULTS = {};
+
+# This is the testing configuration.
+TEST_CONFIG = {};
+
+# Read the files.
+for NAME in list(IMPORTED.keys()):
+	try:
+		DATA = json.load(open(IMPORTED[NAME]))
+	except:
+		DATA = open(IMPORTED[NAME]).read()
+
+	IMPORTED[NAME] = DATA;
+
+# Download the models.
+def download_models():
+	for MODEL_NAME in IMPORTED["Models"].keys():
+		MODEL_ID = IMPORTED["Models"][MODEL_NAME];
+		ollama.pull(MODEL_ID);
+
+# Let the user choose the testing type.
+def select_testing_type():
+	if not('CoT' in list(TEST_CONFIG.keys())):
+		RESPONSE = abs(int(input("Type “1” for a chain-of-thought test or “0” for a classic test: ")));
+		TEST_CONFIG['CoT'] = RESPONSE > 0;
+
+	if not('multi-shot' in list(TEST_CONFIG.keys())):
+		RESPONSE = abs(int(input("Type “1” for a multi-shot test or “0” for a zero-shot test: ")));
+		TEST_CONFIG['multi-shot'] = RESPONSE > 0;
+
+	for CONFIGURATION_TYPE in list(TEST_CONFIG.keys()):
+		if (TEST_CONFIG[CONFIGURATION_TYPE]):
+			print(f"{CONFIGURATION_TYPE}:\tEnabled")
+		else:
+			print(f"{CONFIGURATION_TYPE}:\tDisabled")
+
+	return (TEST_CONFIG);
+
+'''
+Check for questions cache.
+Returns: cache validity
+'''
+def test_questions_cache():
+	CACHE_VALID = False;
+
+	# Check for the validity of the cache files
+	SOURCES = ["tests/cache/config.JSON", "tests/cache/prompts.JSON"];
+	for FILE_NAME in SOURCES:
+		CACHE_VALID = os.path.isfile(FILE_NAME);
+
+		if (not(CACHE_VALID)):
+			break;
+
+	if (CACHE_VALID):
+		try:
+			# Open the last configuration file and check if the used testing configurations are equal.
+			CACHED_DATA = json.load(open('tests/cache/config.JSON'));
+			CACHE_VALID = CACHED_DATA == TEST_CONFIG;
+		except:
+			CACHE_VALID = False;
+
+	if (not(CACHE_VALID)):
+		for FILE_NAME in ["tests/cache/config.JSON", "tests/cache/prompts.JSON", "tests/cache/responses.JSON"]:
+			if (os.path.isfile(FILE_NAME)):
+				# Remove the invalid caches.
+				os.remove(FILE_NAME);
+
+	return (CACHE_VALID);
+
+'''
+Format the questions.
+
+Returns: (dict) the prompts
+'''
+def format_questions():
+	CACHE_USABLE = test_questions_cache();
+
+	if (CACHE_USABLE):
+		print("Using cache.");
+		IMPORTED['Cached Prompts'] = json.load(open("tests/cache/prompts.JSON"));
+		for TYPE_NAME in list(IMPORTED['Strings']['testing'].keys()):
+			if (type(TYPE_NAME) is str):
+				PROMPTS[TYPE_NAME.strip()] = IMPORTED['Cached Prompts'][TYPE_NAME];
+			else:
+				PROMPTS[TYPE_NAME] = IMPORTED['Cached Prompts'][TYPE_NAME];
+	else:
+		print("Generating questions…")
+
+		# Loop through each author type
+		for TYPE_NAME in list(IMPORTED['Strings']['testing'].keys()):
+			PROMPTS[TYPE_NAME] = {};
+
+			# Loop through each source
+			for SOURCE_NAME in list(IMPORTED['Strings']['testing'][TYPE_NAME].keys()):
+				PROMPTS[TYPE_NAME][SOURCE_NAME] = [];
+
+				for PASSAGE in IMPORTED['Strings']['testing'][TYPE_NAME][SOURCE_NAME]:
+					PROMPT = "";
+
+					if TEST_CONFIG['multi-shot']:
+						PROMPT = f"{IMPORTED['Prompts']["sample"]}"
+
+						for GENERATION_TYPE in IMPORTED['Strings']['training'].keys():
+							for TEXT_NUMBER in range(len(IMPORTED['Strings']['training'][GENERATION_TYPE])):
+								PROMPT = f"{PROMPT}\n\n{GENERATION_TYPE}-written #{str(TEXT_NUMBER + 1)}: \n“{'\n\n\t'.join(IMPORTED['Strings']['training'][GENERATION_TYPE][TEXT_NUMBER].strip().split("\n\n"))}”";
+
+							PROMPT = f"{PROMPT}\n\n{IMPORTED['Prompts']['bridge']}\n\n";
+
+					PROMPT = f"{PROMPT}{IMPORTED['Prompts']["introduction"]}\n\n“{'\n\t'.join(PASSAGE.strip().split("\n"))}”\n\n{IMPORTED['Prompts']["classify"]}"
+
+					PROMPTS[TYPE_NAME][SOURCE_NAME].append(PROMPT);
+
+	create_cache(exclude=['responses']);
+
+	return(IMPORTED['Prompts']);
+
+def asking_execution():
+	if (screen_asking()):
+		ask_AI();
+		save_responses();
+
+'''
+This function will request the user’s final review before running the LLMs. This function also ensures that testing conditions are satisfied before executing.
+
+Returns: (bool) user's proceed state
+'''
+def screen_asking():
+	RESPONSE = '';
+
+	# Check the testing conditions.
+	CONTINUE = len(list(IMPORTED['Models'].keys())) > 0;
+
+	if (CONTINUE):
+		try:
+			RESPONSE = input("\n\nDo you now want to begin interaction with the LLMs? \nThis process will take about 20 minutes. \n");
+		except KeyboardInterrupt:
+			CONTINUE = False;
+		else:
+			if ("n" in RESPONSE.lower().strip().rstrip('.').rstrip('!')):
+				CONTINUE = False;
+	else:
+		print("No testing models configured. Change that configuration and run this script again once you're ready.")
+
+	return CONTINUE;
+
+'''
+Ask the AI.
+
+Parameters:
+	models (array): the models to test
+Returns: (dict) the responses
+'''
+def ask_AI():
+	# Loop through each author type
+	for TYPE_NAME in list(PROMPTS.keys()):
+		RESPONSES[TYPE_NAME] = {};
+
+		# Loop through each source
+		for SOURCE_NAME in list(PROMPTS[TYPE_NAME].keys()):
+			RESPONSES[TYPE_NAME][SOURCE_NAME] = [];
+
+			print("\n");
+			TARGET_LENGTH = len(PROMPTS[TYPE_NAME][SOURCE_NAME]);
+
+			for PROMPT_NUMBER in range(TARGET_LENGTH - 1):
+				print(f"\033[FAnswering prompt {PROMPT_NUMBER + 1} of {TARGET_LENGTH} from {TYPE_NAME} work in {SOURCE_NAME}…");
+				PROMPT = PROMPTS[TYPE_NAME][SOURCE_NAME][PROMPT_NUMBER];
+				MODEL_RESPONSES = {};
+
+				for MODEL_NAME in list(IMPORTED['Models'].keys()):
+					# Get the model ID.
+					MODEL_ID = IMPORTED['Models'][MODEL_NAME];
+
+					# Send update log.
+					print(f"\033[FAnswering prompt {PROMPT_NUMBER + 1} of {TARGET_LENGTH} from {TYPE_NAME} work in {SOURCE_NAME} using {MODEL_NAME}…");
+
+					# Prepare the messages.
+					MESSAGES = {};
+
+					# Set the messages.
+					MESSAGES['User'] = [];
+					MESSAGES['Model'] = [];
+					MESSAGES['History'] = [];
+
+					# Add the order of the messages.
+					MESSAGES['User'].append(PROMPT);
+					(MESSAGES['User'].append(IMPORTED['Prompts']['judge'])) if (TEST_CONFIG['CoT']) else False;
+					MESSAGES['User'].append(IMPORTED['Prompts']['answer format']);
+
+					for MESSAGE in (MESSAGES['User']):
+						# Add the message.
+						MESSAGES['History'].append({'role': 'user', 'content': MESSAGE});
+
+						MESSAGE_LAST = ((ollama.chat(model=MODEL_ID, messages=MESSAGES['History']))['message']['content']).strip("\t\n").strip();
+						MESSAGES['Model'].append(MESSAGE_LAST);
+						MESSAGES['History'].append({'role': 'assistant', 'content': MESSAGE_LAST});
+
+
+					# Associate with the correct LLM model.
+					del MESSAGES['User'];
+					del MESSAGES['History'];
+					MODEL_RESPONSES[MODEL_NAME] = MESSAGES['Model'];
+
+				# Append the messages.
+				RESPONSES[TYPE_NAME][SOURCE_NAME].append(MODEL_RESPONSES);
+
+				# Cache the responses.
+				create_cache(include=['responses']);
+
+				# Update the status.
+				print(f"\033[FAnswered prompt {PROMPT_NUMBER + 1} of {TARGET_LENGTH} from {TYPE_NAME} work in {SOURCE_NAME}.");
+			print(f"\033[FFinished answering all {TARGET_LENGTH} prompts consisting of {TYPE_NAME} work in {SOURCE_NAME}.");
+	return False;
+
+'''
+Generate a cache.
+
+Parameters:
+	exclude (list): Exclude items
+	include (list): Include certain items
+'''
+def create_cache(**params):
+	if (not('test config' in params['exclude']) if ('exclude' in list(params.keys())) else (('test config' in params['include']) if ('include' in list(params.keys())) else True)):
+		save_data(dictionary=TEST_CONFIG, filename='tests/cache/config.JSON');
+	if (not('prompts' in params['exclude']) if ('exclude' in list(params.keys())) else (('prompts' in params['include']) if ('include' in list(params.keys())) else True)):
+		save_data(dictionary=PROMPTS, filename='tests/cache/prompts.JSON');
+	if (not('responses' in params['exclude']) if ('exclude' in list(params.keys())) else (('responses' in params['include']) if ('include' in list(params.keys())) else True)):
+		save_data(dictionary=RESPONSES, filename="tests/cache/responses.JSON");
+
+'''
+Export the responses.
+'''
+def save_responses():
+	save_data(dictionary=RESPONSES, filename=f"tests/outputs/responses{' multi-shot' if (TEST_CONFIG['multi-shot'] if 'multi-shot' in list(TEST_CONFIG.keys()) else False) else ''}{' CoT' if (TEST_CONFIG['CoT'] if 'CoT' in list(TEST_CONFIG.keys()) else False) else ''} {str(datetime.datetime.now().time())}.JSON");
+	os.remove("tests/cache/responses.JSON");
+
+'''
+Save the data.
+
+Parameters:
+	filename (str): The file name
+'''
+def save_data(**parameters):
+	if (parameters['filename'].strip()):
+		with open(parameters['filename'], 'w') as file:
+			# print(f"Saving {parameters['filename']}…");
+			json.dump(parameters['dictionary'], file);
+
+
+# Run the code.
+def main():
+	select_testing_type();
+	download_models();
+	format_questions();
+	asking_execution();
+
+main();