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In a retail e-commerce company, the team was building an AI customer support agent to handle common queries like order status, returns, refunds, and product information. In the beginning, the prompt was tested manually with a few sample questions, and everything seemed to work fine.<\/p>\n
However, end users don’t type like QA testers. They use shortcuts, mix multiple questions into one sentence, include slang when frustrated, and often skip punctuation. A prompt that worked well in controlled testing started missing the intent in real scenarios, giving responses that felt robotic or incorrect. Instead of reducing support workload, this led to more confusion and increased escalations to human agents.<\/p>\n
One of the biggest challenges was inconsistency in responses. Customers would ask similar questions in different ways, such as “Where is my order?” “Track my shipment”, or “Can you tell me my delivery status?”, but the agent would sometimes return different or inaccurate answers. In some cases, it failed to recognize the intent entirely and responded irrelevantly, which frustrated users.<\/p>\n
Another issue was the unpredictability of user inputs. Real users often type informally, make typos, or use incomplete sentences. While the agent performed well with structured test queries, it struggled with these real-world variations. This gap between controlled testing and actual usage became very noticeable.<\/p>\n
Scalability challenges in testing also emerged. Manually validating every variation of a question was time-consuming and inefficient. As the number of supported scenarios increased, it became nearly impossible to test everything, leading to gaps that only surfaced in production. To address this, batch testing for prompts was introduced. Real customer queries were collected from past chat logs and support tickets, including variations, unclear queries, and edge cases. Instead of testing individually, these were run in bulk and the responses analysed.<\/p>\n
This approach helped quickly identify where the agent was failing, whether in understanding intent, selecting the correct response, or handling variations. Based on these insights, the prompts were refined, intent handling was improved, and responses became more consistent. After these improvements, the agent started delivering more accurate and reliable answers. It also reduced manual testing effort and made it easier to validate future updates. Overall, this approach helped build a more stable and user-friendly experience that better aligned with real customer behaviour.<\/p>\n
Steps to define the Test Cases<\/strong><\/p>\n When you click on test hub in below screenshot you can see the view and options test hub provide for test cases.<\/p>\n You can simply create a list of test cases or prompts<\/p>\n Example:<\/p>\n Include:<\/p>\n So, there are some ways to create the test cases like, Upload test case, generate test cases, use activity data and in below screenshot you can see the test cases which was manually written or added.<\/p>\n When you have done with your set of test cases then,<\/p>\n Run Batch Test<\/strong><\/p>\n Analyze Results<\/strong><\/p>\n If your test cases are failing it will give detailed summary of search result (As shown in below screenshot) to you from that you can Improve Your Bot<\/p>\n Based on results:<\/p>\n You can Re-Run again after changes Compare results \u2192 check there is any improvement or not.<\/p>\n Conclusion <\/strong><\/p>\n Batch testing prompts in Microsoft Copilot Studio makes bot testing faster, smarter, and more reliable. Instead of checking a few queries manually, it allows us to validate real-world user scenarios at scale and quickly identify gaps in responses in a single click.<\/p>\n It helps ensure consistency, improves accuracy, and reduces the chances of unexpected behavior once the bot is live. Over time, it becomes an essential part of maintaining quality and continuously improving the chatbot experience, making it more dependable and aligned with real user needs.<\/p>\n In a retail e-commerce company, the team was building an AI customer support agent to handle common queries like order status, returns, refunds, and product information. In the beginning, the prompt was tested manually with a few sample questions, and everything seemed to work fine. However, end users don’t type like QA testers. They use\u2026 Read More: How Batch Testing Prompts Helps You Build Reliable Copilot Agents »<\/a><\/span><\/p>\n","protected":false},"author":15,"featured_media":0,"comment_status":"closed","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[3426,2361],"tags":[3427],"class_list":["post-46162","post","type-post","status-publish","format-standard","hentry","category-copilot-agents","category-technical","tag-build-reliable-copilot-agents"],"aioseo_notices":[],"aioseo_head":"\n\t\t\n\t\n\t\n\t\n\t\n\t\n\t\t\n\t\t\n\t\t\n\t\t\n\t\t\n\t\t\n\t\t\n\t\t\n\t\t\n\t\t\n\t\t\n\t\t\n\t\t\n\t\t\n\t\t\n\t\t\n\t\t\n\t\t\n\t\t\n\t\t\n\t\t\n\t\t\n\t\t\n\t\t\n
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\nPrepare Your Test Cases or test set<\/strong><\/p>\n\n
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<\/p>\nFAQs<\/strong><\/h3>\n
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\nBatch testing lets you run multiple test cases or prompts at once instead of validating them one at a time, so you can quickly see how your Copilot agent handles a wide range of real user queries.<\/li>\n
\nManual testing typically uses a small, controlled set of sample questions. Real users type differently, with typos, slang, mixed intents, and incomplete sentences, so a prompt that passes manual testing can still fail in production.<\/li>\n
\nTest cases can be manually written, uploaded, generated automatically, or pulled from real activity data such as past chat logs and support tickets.<\/li>\n
\nIt surfaces incorrect or irrelevant answers, wrong intent detection, missing responses, and inconsistent answers across similarly worded queries.<\/li>\n<\/ol>\n","protected":false},"excerpt":{"rendered":"