How to Test Your AI Chatbot for More Reliable Answers

A customer asks your chatbot, “What time do you close?”

It answers at 8 p.m. Your shop actually closes at 6. The customer arrives at 7, finds the door locked, and doesn’t come back.

This is the risk most businesses worry about when they consider an AI chatbot. In a December 2025 survey by Sony Biz Networks of 100 IT staff at mid-sized Japanese companies, the top concern was wrong answers (37.0%).

You can’t make any AI perfect. But you can catch mistakes before your customers do. Here’s how the Kotae team does it, using a testing tool called LangSmith, and what you can learn from it even if you never touch a line of code.

What is AI chatbot testing?

AI chatbot testing means giving your AI an exam before it talks to real customers. It is like having a new staff member practice with role-play before they start working the front desk.

Normal software gives the same output for the same input every time. AI does not. The same question can get a slightly different answer each time. Changes to prompts, models, or application logic can affect output quality, which is why regression testing matters. 

So testing AI is not a one-time job. You need to test again every time you change something.

What is LangSmith?

LangSmith's tracing dashboard monitors error rates and latency metrics across production and development environments.

LangSmith is a testing and monitoring tool for teams that build AI apps. It is made by a company called LangChain. It does two main jobs:

• Recording (tracing): It saves each step the AI takes to reach an answer.

• Scoring (evaluation): It gives each answer a score, so you can see quality as numbers.

A test needs three parts:

• Dataset: a set of test questions, with examples of good answers

• Target: the chatbot, or one part of it

• Evaluators: the rules or tools that score each answer

The AI testing process combines datasets, target chatbots, and evaluators to systematically evaluate model accuracy and performance.

In short: a question sheet, a test taker, and a grader. With those three, you can give your AI an exam.

How do you score AI answers?

There are three main ways:

• Rule-based scoring: Check things with a clear yes or no. For example, "Does the answer include the correct phone number?"

• AI scoring AI (LLM as a judge): Ask a second AI, "Does this answer fit the question?" This lets you check many answers quickly.

• Human review: People check important or tricky answers by hand.

According to LangChain's State of Agent Engineering survey (November to December 2025, 1,340 people), among teams that run evaluations, 59.8% use human review and 53.3% use LLM as a judge. AI for speed and coverage, people for depth. That mix is now the normal approach.

How does Kotae test with LangSmith?

The experiment dashboard tracks evaluator scores, latency metrics, and token usage across multiple dataset test runs.

The Kotae team repeats these steps with LangSmith:

1. Build a set of test questions: We collect questions real customers are likely to ask, such as business hours, prices, and how to book.

2. Test everything before each update: When we change a prompt or a model, we run the full question set and score the answers.

3. Compare scores with the last version: If scores drop, we find the cause, fix it, and test again.

4. Watch live conversations after launch: We record real conversations and check for strange answers.

5. Add new problems to the test set: So the same mistake does not happen twice.

Step 5 is the key. Your test set gets stronger the more you use it.

Kotae's 5-step testing workflow establishes a continuous improvement loop by feeding live conversation issues back into the test dataset.

Online evaluation is still less common than offline evaluation. In the same LangChain survey, 52.4% run tests before launch (offline evaluation), and only 37.3% check live conversations (online evaluation).

Can testing stop AI hallucinations?

To be honest, not completely. A hallucination is when AI gives an answer that sounds right but is false. With current AI systems, this risk cannot be eliminated completely. 

But testing helps you:

• Find questions the AI often gets wrong, before launch

• Check with numbers that a fix really helped

• Make sure a new change does not break answers that used to be right

In the LangChain survey, quality was the top barrier to running AI in production (32%). Quality does not end when you pick a model. You protect it by testing, again and again.

An AI you do not test is like a new hire sent to the front desk with no training.

Want to see how Kotae answers the questions your customers ask most? Try it free and see for yourself.