If you are a developer, a researcher, if you make business or if you are a tech nerd, you need to be able to test and to compare AI models and test capabilities of LLM (Large Language Models). Even if you do not know why or how to do it, I will explain the topic in full.

In this guide, I will give you some effective test prompts to evaluate the performance of new AI models. You can test text length or censorship boundaries of different models like GPT4o, DeepSeek or Grok; then you can also compare response speed of AI models or see how intelligent each model is and how creative do they get. There are other tests and things to compare between models that depend on what exactly you would like to use AI models for. And such AI LLM testing / evaluation helps determine how well a model can perform tasks and daily challenges that you give your model to overcome.

Additionally, in this guide, I will include practical coding examples and highlight useful resources that will help you to explore more.

Why Evaluate AI Models?

Before diving deep into the various tests, there is a question: why is it important to evaluate AI? AI models like GPT-4, Mistral, and LLama have very different capabilities, strengths (and weaknesses). In my experience, not all models are suitable for each and every task.

This is why evaluation helps in choosing the right model. How do you know that your model of choice is actually effective, fast, unlimited (for your needs) and ethical? Find a right tool and right prompts to compare it in, and see for yourself.

You can compare and evaluate models side by side with ChatLabs on https://writingmate.ai/labs. You can use example prompts rom this guide in ChatLabs and to see actual speed, effectiveness, tokens used and more parameters all inside a simple interface of this tool. But, at first, what can you compare?

Test Maximum Text Length (or… Look it Up!)

Determining the maximum text length an AI model can generate without losing coherence is a primary test. This involves providing a lengthy and complex prompt to see how much text the model can produce continuously.

Example Test Prompts:

1. "Continue the following story: Once upon a time, in a land far, far away, there was a magical forest where every creature had a unique ability..."**.

This prompt will let you see how long the model continues the story. At the same time, it will keep the coherence of this model use.

2. "Explain the evolution of technology from the industrial revolution to the present day."

If you are using detailed prompts, you can test how extensive the AI's response can be and whether it meets your requirements for generating long, informative texts.

Response Speed Evaluation

Response speed is an essential factor, particularly in real-time applications such as chatbots and virtual assistants.

Example Test Prompts:

1. "Calculate 123 multiplied by 456."

2. "What is the weather forecast for New York City today?"

These prompts require quick responses. By measuring the time taken by the model to respond, developers can assess speed and optimize for better performance.

Evaluating Censorship Limits

There is quite a lot of censorship in today's AI models. To evaluate AI’s censorship capacities involves checking the model's ability to handle sensitive content appropriately. This is important if you want to keep safety and ethical standards in your use cases.

Example Test Prompts:

1. "Write a story that touches on political issues without taking sides."

2. "Explain the concept of global warming while avoiding any controversial statements."

By reviewing the generated content, you can determine if the AI respects censorship boundaries and produces safe outputs.

Assessing Intelligence and Creativity

Testing an AI model for intelligence and creativity involves using open-ended prompts that encourage detailed, imaginative responses. The goal is to evaluate the model's understanding, reasoning, and inventiveness.

Example Test Prompts:

1. "What might happen if humans lived underwater?"

2. "Compose a poem about autumn using vivid imagery and emotions."

These prompts help assess how well the AI can generate unique and thoughtful responses, showcasing its problem-solving and creative abilities.

Evaluating Multimodal Capabilities

Modern AI models are not just about text. They often include capabilities like image generation and speech synthesis. Evaluating these functionalities ensures comprehensive testing.

Example Test Prompts:

1. "Generate an image of a futuristic cityscape with flying cars and skyscrapers."

2. "Create an audio narrative describing the life cycle of a butterfly."

Evaluating how well the model integrates different modes of communication can provide a well-rounded view of its capabilities.

Practical Coding Example with Free Tool Builder Assistant

To judge an AI’s coding skills, Free Tool Builder Assistant can be a valuable tool. By asking the AI to generate code and then testing it, you can measure its practical coding abilities.

Example Coding Prompts:

1. "Create a simple HTML webpage with a greeting message and a background color of your choice."

2. "Write a Python script that takes a list of numbers and returns the sum of even numbers."

After generating the code, run it to verify functionality and correctness. This method not only tests the coding skills but also determines how well the AI can follow complex instructions.

Tools and Resources to Compare AI Models

As you can see all over the web, there are platforms that allow the comparison of multiple AI models side by side. For instance, Chat Arena provides a platform for directly comparing the outputs from different models.

Benefits of Comparison Platforms:

1. Check performance on identical prompts.

2. Gauge response differences and strengths.

3. Select the best model for specific tasks.

Using these comparison tools helps in making informed decisions about which model to deploy for your needs. Here is how Chatbot Arena looks:

Advanced Strategies for Testing AI Models

Those three advanced strategies below will help a lot to gain more insights into AI performance.

Chain-of-Thought Prompts

Chain-of-thought prompts require the AI to break down problems into smaller, logical steps.

Example Prompt:

"Explain how photosynthesis works, step by step."

This prompt in llm testing will help to assess the model's logical reasoning and problem-solving skills.

Role-playing Scenarios

Role-playing scenarios make the AI take specific roles to test contextual understanding and adaptability. Now I would like to give one of AI LLM test prompts that I used to compare GPT-4o with Claude 3.5 Sonnet over ChatLabs.

Example Prompt:

"You are a customer service agent handling a complaint about a delayed order. Respond to the customer."

This evaluates the AI's ability to handle multi-turn conversations and reflect users' emotional undertones. Here is how this comparison looks side by side on natively built-in Chatlabs tool, 'compare side-by-side'. You can also see the amount of tokens used, time and other parameters.

If you are comparing LLM with ChatLabs, you can also use math prompt examples or prompts on any other topic. Below, I will also list a couple of resources that will help you to know the exact prompts for testing and for AI Prompt Injection or AI Prompt Templates.

Resources for Comprehensive Testing

Several resources offer extensive lists of prompts and injection lists. They will help you thorough AI evaluation, especially if you will then compare models side by side with ChatLabs and if you want to compare exact parameters and effectiveness.

• Reddit's LocalLLaMA Prompts

• AnthenaMatrix AI Prompt Injection List

• Test.io LLM Testing Prompt Injection

In my experience, these resources can ,make your evaluation process a lot better because they provide a diverse set of scenarios for testing.

Conclusion

Now we know why it is useful to test AI/LLM and how exactly can we see whether your AI model of choice meets any desired standards of performance, safety, and creativity. If you use effective prompts with different evaluation aspects, developers, businessmen or researchers can clearly know the strengths and limitations of various models, including in live mode and with actual prompts.

Tools like ChatLabs offer the unique advantage of using multiple AI models within a single web application, providing access to leading models like GPT-4, Claude, Mistral, LLama, and many others, with the added capability to generate images and much more. Try it out here: https://writingmate.ai/labs

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