Margarita Simonova is the founder of ILoveMyQA.com.
getty
The endeavor of taming language learning models (LLMs) to serve the purposes of your organization can be a tricky process. The unpredictability of these wonders of artificial intelligence (AI) can test the patience of any prompt engineer.
However, there are systematic ways that one can employ to help turn these wild machines into trusty companions. In this article, we will take a look at some of the best techniques to refine and validate input prompts given to AI so they can yield accurate and relevant results.
Clarity And Comprehension Testing
Just as you would talk to a human colleague, when communicating with a machine, you need to make sure that you are speaking to it as clearly as possible. Clarity and comprehension testing can ensure the model understands your prompt clearly and responds accurately. It also looks at the LLM’s output for clarity and comprehension.
To do this type of testing, you can try asking an LLM the same question but worded differently and see if you get the same response. For example, you can first use a prompt such as, “Explain the impact of climate change on agriculture.” Then, you can ask, “How does climate change affect farming?” You can then compare the responses to see if the model interpreted the question correctly and produced easy-to-understand output.
Response Consistency Testing
Just as you would expect reliability from a friend, LLMs should produce consistent results so they can be trusted day in and day out. This consistency should occur even if prompts are slightly varied from their original versions.
This type of response consistency testing and prompt variability testing can be performed by verifying that models produce similar responses even when they are given slightly different prompts. For example, you can run a prompt like. “List the benefits of exercising.” Then you can ask, “What are the benefits of exercising?” Check to see if the responses are consistently aligned, covering benefits without contradicting each other.
Length Optimization TestingThere are times when a thorough and detailed response from an LLM is needed—and other times when just a few words will suffice. The length of the response should fit key phrases in the prompt, such as “summarize,” “give a detailed analysis” or “respond in 500 words.”
Length optimization testing is used to see how prompts affect response length. As an example, try asking the model to “summarize the plot of Pride and Prejudice.” Then, you can prompt it to “in one paragraph, summarize the main events in Pride and Prejudice.” Comparing responses will help you find the ideal way to get responses that are the right length you are looking for.
Bias And Fairness Testing
Although models have vast minds, their interpretation of this library of knowledge echoes their human creators. Oftentimes, prompts might touch on sensitive topics, and an LLM should try to make as impartial an answer as possible.
Bias and fairness testing, along with sensitivity and appropriateness testing, aims to ensure that prompts do not yield biased or insensitive results. As an example, the prompt “What is an ideal employee?” can be tested for answers that include gender or ethnicity to confirm that the model’s response is neutral and unbiased.
Use Case Specific Testing
While an LLM is often a jack-of-all-trades, an organization often needs it to master just one or two. Common use cases include customer support or IT help desks. In these cases, it can be safer and more efficient to limit the scope of the LLM.
The goal of use case-specific testing is to validate prompts for the organization’s particular domains. Examples of this can include writing prompts such as “Write a Python function for a binary search.” Compare that to a prompt such as, “Can you show me a binary search in Python?” Evaluating these responses can show if they deliver accurate results within the domain of computer programming.
Complex Prompt Breakdown Testing
Many of the problems that we need to ask an LLM to address are complex and difficult to convey in a prompt. To get these intricate requests correct, it is important to break down the prompt into clear sections that the LLM can tackle in turn.
Testing for complex prompt breakdowns involves evaluating how the model handles prompts with multiple instructions. For example, giving a prompt such as, “Summarize the advantages of renewable energy, list the top countries using it and suggest three future improvements,” can test if the model is addressing all the components in the prompt correctly.
Creativity And Tone Testing
LLM models are not only asked to return accurate and reliable results but are also expected to create novel responses that rival the creativity of human minds. Prompt engineers can demand that LLMs have any range of personalities such as playfulness, humorous or philosophical.
Testing for creativity and tone has the goal of seeing how a model can respond in different tones or styles based on prompt wording. For example, typing “Explain photosynthesis to a 5th grader” or “Make photosynthesis fun and easy for kids to understand” will give you two ways to assess whether the model responds to the prompt with appropriate results.
Context Retention Testing
When talking with a model, you expect them to have the memory of an elephant rather than a goldfish. It can be frustrating to have to remind models of the type of output you want.
Context retention testing involves determining a model’s ability to retain and build context from previous prompts in a conversation. One way to do this is to start with a prompt such as, “Tell me about the history of space travel,” and then follow up with a prompt such as, “What about recent developments?” That can show if the model is continuing from the first prompt and if they avoid repeating details.
Conclusion
Knowing the right prompts to use can make or break an organization’s use of LLMs. The types of prompt tests mentioned here help fine-tune the interactions with AI, ensuring that prompts consistently deliver reliable and contextually appropriate responses.
Forbes Technology Council is an invitation-only community for world-class CIOs, CTOs and technology executives. Do I qualify?
{Categories} _Category: Inspiration{/Categories}
{URL}https://www.forbes.com/councils/forbestechcouncil/2024/12/27/ai-prompt-testing-for-organizations-that-depend-on-llms/{/URL}
{Author}Margarita Simonova, CommunityVoice{/Author}
{Image}https://imageio.forbes.com/specials-images/imageserve/67378d727aef179a45da462e/0x0.jpg?format=jpg&height=600&width=1200&fit=bounds{/Image}
{Keywords}Innovation,/innovation,Innovation,/innovation,technology,standard{/Keywords}
{Source}Inspiration{/Source}
{Thumb}{/Thumb}