8 Key Factors to Consider When Testing AI Chatbots for Accuracy – MUO – MakeUseOf

You can test different AI chatbots to determine which works best. But how should you do this? Here are some key factors to consider.
AI has come a long way from producing irrelevant, incoherent output. Modern chatbots use advanced language models that answer general knowledge questions, compose lengthy essays, and write code, among other complex tasks.
Despite these advancements, note that even the most sophisticated systems have limitations. AI still makes mistakes. To determine which chatbots are least prone to hallucinations, test their accuracy based on these factors.
Run math equations through chatbots. They’ll test the platform’s ability to analyze word problems, translate mathematical concepts, and apply correct formulas. Only a few models demonstrate reliable numeracy. In fact, one of ChatGPT’s worst issues during its first months was its terrible math comprehension.
The below image shows ChatGPT failing at basic statistics.
ChatGPT showed improvement after OpenAI rolled out its May 2023 updates. But considering its limited datasets, you’ll still have trouble with intermediate to advanced mathematical computations.
Meanwhile, Bing Chat and Google Bard show better numeracy. They run queries through their respective search engines, enabling them to pull formulas and answer sheets.
Try rephrasing your word problems. Avoid lengthy sentences and replace weak verbs; otherwise, chatbots might misunderstand your questions.
Modern AI systems can take on multiple tasks. Advanced LLMs enable them to retain previous instructions and answer prompts by section, whereas older systems process singular commands. For instance, Siri answers one question at a time.
Feed chatbots three to five tasks simultaneously to test how well they analyze complex prompts. Less sophisticated models can’t process that much information. The below image shows HuggingChat malfunctioning at a three-step prompt—it stops at step one and deviates from the topic.
HuggingChat’s last lines are already incoherent.
ChatGPT quickly completes the same prompt, generating error-free, intelligent responses at every step.
Bing Chat provides a condensed answer to the three steps. Its rigid restrictions prohibit unnecessarily lengthy outputs that waste processing power.
Since AI training costs massive resources, most developers limit datasets to specific periods. Take ChatGPT as an example. It has a knowledge cut-off of September 2021—you can’t request weather updates, news reports, or recent developments. Here’s ChatGPT saying it has no access to real-time information.
Bard has access to the internet. It pulls data from Google SERPs, so you can ask a broader range of questions, e.g., recent events, news, and predictions.
Likewise, Bing Chat pulls real-time information from its search engine.
Bing Chat and Bard deliver timely, up-to-date information, but the latter provides more detailed responses. Bing merely presents data as is. You’ll notice that its outputs often match the phrasing and tone of its linked sources verbatim.
Chatbots must provide relevant outputs. They should consider the literal and contextual meaning of your prompts when responding. Take this conversation as an example. Our persona needs a new phone, but only has $1,000—ChatGPT doesn’t exceed the budget.
When testing for relevance, try crafting lengthy instructions. Less sophisticated chatbots tend to go off on a tangent when fed confusing instructions. For instance, HuggingChat can compose fictional stories. But it might deviate from the main topic if you set too many rules and guidelines.
Contextual memory helps AI produce accurate, reliable output. Instead of taking your questions at face value, they string together the details you mention. Take this conversation as an example. Bing Chat connects two separate messages to form a helpful, concise response.
Likewise, contextual memory allows chatbots to remember instructions. This image shows ChatGPT mimicking the way a fictional character talks throughout several chats.
Test this function yourself by consistently referencing previous statements. Feed chatbots various information, then force them to recall these in later responses.
Contextual memory is limited. Bing Chat starts new conversations every 20 turns, while ChatGPT can’t process prompts over 3,000 tokens.
AI doesn’t always do as intended. Faulty training could cause machine learning technologies to commit various mistakes, from minor math errors to problematic comments. Take Microsoft Tay as an example. Twitter users exploited its unsupervised learning model and conditioned it into saying racial slurs.
Thankfully, global tech leaders learned from Microsoft’s blunder. Although cost-efficient and convenient, unsupervised learning leaves AI systems prone to deception. Hence, developers primarily rely on supervised learning nowadays. Chatbots like ChatGPT still learn from conversations, but their trainers filter information first.
Expect differing guidelines from AI companies. ChatGPT’s less rigid restrictions accommodate a broader range of tasks, but are weak against exploitation. Meanwhile, Bing Chat follows stricter limits. While they help combat exploitation attempts, they also impede functionality. Bing automatically shuts down potentially harmful conversations.
AI is inherently neutral. Its lack of preferences and emotions makes it incapable of forming opinions—it merely presents information it knows. Here’s how ChatGPT responds to subjective topics.
Despite this neutrality, AI biases still arise. They stem from the patterns, datasets, algorithms, and models that developers use. AI might be impartial, but humans aren’t.
For instance, The Brookings Institution claims that ChatGPT demonstrates left-wing political biases. OpenAI denies these allegations, of course. But to avoid similar issues with newer models, ChatGPT avoids opinionated outputs altogether.
Likewise, Bing Chat avoids sensitive, subjective matters.
Assess AI biases yourself by asking opinion-based, open-ended questions. Talk about topics with no right or wrong answer—less sophisticated chatbots will likely display baseless preferences toward specific groups.
AI rarely double-checks facts. It merely pulls information from its datasets and rephrases them through language models. Unfortunately, limited training causes AI hallucinations. You can still use generative AI tools for research, but make sure you verify facts yourself. Take the output with a grain of salt.
Bing Chat simplifies the fact-checking process by listing its references after every output.
Bard AI doesn’t list its sources but generates updated, in-depth explanations by running Google search queries. You’ll get the main points from SERPs.
ChatGPT is prone to inaccuracies. Its 2021 knowledge cut-off prevents it from answering questions about recent events and incidents.
AI isn’t the be-all and end-all of technology. While sophisticated AI systems and language models perform impressive feats, they also commit errors and inconsistencies. View chatbots with skepticism. You can only utilize AI-driven platforms if you understand their functions and limitations.
Although there are dozens of chatbots across platforms, their reliability and precision might disappoint you. You’ll merely waste time testing them. To ensure quality results, we suggest focusing on the three most robust models on the market: ChatGPT, Bing AI, and Google Bard.

Jose Luansing Jr. is a staff writer at MUO. He has written thousands of articles on tech, freelance tools, career advancement, business, AI, and finance since 2017.

As a writer, Jose’s goal is to share advice on self-improvement and upskilling. He helps readers understand the real-life applications of various systems, plus how these support career advancement.

Recently, Jose has also been testing AI systems. He believes that AI is inherently unbiased—all hallucinations, inconsistencies, and security risks stem from humans.