Prompt Debugging Techniques: Reduce Hallucinations and Improve LLM Accuracy

In the context of large language models, anything in a prompt that causes the model to:

Large language models are becoming increasingly sophisticated and capable, demonstrating improved understanding and more accurate responses over time. For example, the following prompt doesn't cause the model to hallucinate

The model doesn't fall for our bait and actually responds with "The Eiffel Tower is located in Paris, France. So, what is a good example of a buggy prompt?

Let's invent a word called TLMIO. This word doesn't mean anything in the AI context, at least. Let's write a prompt and ask the model to explain it

As you see, the model is making up a response with non-related articles about it.

Or what about the following prompt

The latest version of Python today (June 16th, 2025) is 3.13.5 but the model response is 3.10

When working with large language models, one common stumbling block is hallucinations. In the context of large language models (LLMs), hallucinations happen when the model generates information that sounds plausible but is actually false or nonsensical. Hallucinations occur because LLMs predict text based on patterns in their training data, not on verified facts. This makes them great storytellers but sometimes unreliable fact-checkers. Here’s a quick analogy: think of an LLM like a parrot trained on thousands of books. It repeats what it has “heard” but doesn’t truly understand the truth behind the words. If asked about a topic it only partially knows,” it might mix facts and fiction, resulting in hallucination.

Fortune.com in 2023 wrote this article about Two Lawyers fined $5,000 for using ChatGPT hallucination. They cited six fictitious cases generated by ChatGPT that put them in big trouble.

You can also read Air Canada's chatbot lied to a grieving passenger about a discount

As you see, debugging and finding cases that can cause our model to hallucinate is really important, and not having a plan for them during the development of our AI application can bring a serious disaster.

Prompt Engineering plays a crucial role in shaping how models understand and respond to our requests. When working with large language models (LLMs), the quality of the prompt directly influences the output. Think of it like giving directions to a GPS: vague instructions lead to wrong turns, while clear, detailed prompts guide the AI to the right destination.

Prompt engineering provides the tools and techniques to systematically improve prompts:

So, prompt engineering is both the design and the fixing process. Debugging is a key part of engineering — it’s how you improve prompt quality step-by-step.

Debugging in software engineering is the process of:

Let's apply this process to a scenario for a customer support chatbot.

We are developing Acme's Shop Customer Support Chatbot, where users can ask questions about our refund policy. Our refund policy is very simple, like the following:

Let's think about the cases in which our model can fail. What if the user asks:

So, what should be our model behaviour for these questions? Let's use prompt engineering techniques like role playing, few-shot, context injection, guardrails, and intent classification to debug our prompt.

Using Intent classification with few shots (a couple of examples), let's define a list of possible intents and ask the model to categorize the user intent before it responds to them

Now that our model is capable of classifying users' intent, let's add context, which is our refund policy, and instruct the model how to respond to the unrelated questions

Users can ask very ambiguous questions like "Can I get a refund?" without giving the model more details about their case. In this scenario, we should put a guardrail so our model doesn't drift.

When users asks questions that are not directly mentioned in our refund policy like "What if the item I received is damaged? Can I get a refund or replacement?" the LLM still tries to answer those question, so our job is to instruct the model to admit it doesn't know the answer instead of guess and invent something. In this case, we redirect the user to customer support email, or we can implement an AI Agent to access user information and return refund data

We provided all of our instructions in only one prompt to the model. The best approach is to create different prompts for classifying the user's intent first and feed the response of that prompt to the next prompt, which is in charge of responding to the user's intent. We should also implement a product classifier prompt to instruct the model about digital and physical products. We can also take advantage of implementing user agents to access the user's profile to follow up with existing refund requests, for example.

Prompt debugging is essential for harnessing the full potential of large language models. By systematically identifying and fixing issues like hallucinations and inaccuracies, you can significantly improve the reliability and effectiveness of your AI-powered workflows. With careful prompt design and iterative testing, your chatbot or application will deliver clearer, more accurate, and user-friendly responses, building trust and enhancing user experience.