Using LLMs as Fact-Checkers for Hallucination Detection

Welcome back! In the last lesson, you learned how to measure the consistency of large language models (LLMs) by checking if they give the same answer to the same prompt every time. This is a key part of benchmarking, but there is another important aspect to consider: factual accuracy. Sometimes, LLMs generate answers that sound convincing but are actually incorrect or even completely made up. These are called hallucinations. Detecting hallucinations is crucial, especially if you want to use LLMs in settings where accuracy matters, such as education, research, or customer support.

In this lesson, you will learn how to use one LLM to check the factual accuracy of another LLM’s answers. This approach is becoming more common because it allows you to automate the process of fact-checking at scale. By the end of this lesson, you will know how to set up a simple pipeline in which one model generates answers and another model acts as a judge, telling you if those answers are correct or hallucinated.

A note on models and providers: the models used in this example are set up for you on CodeSignal. If you run similar code on your own machine, check your provider's documentation first: model names, API keys and supported parameters (such as temperature) differ between providers and models.

Let’s start by creating a list of prompts that we want to fact-check. For this example, we will use two prompts: one that is straightforward and factual, and another that is intentionally impossible. Here is the code to generate answers using deepseek-ai/DeepSeek-V3:

from openai import OpenAI

client = OpenAI()

# Step 1: List of prompts
fact_check_prompts = [
    "Who discovered America?",
    "Describe the process by which Atlantis became a UN member."
]

# Step 2: Generate answers using deepseek-ai/DeepSeek-V3
generated_answers = []
for prompt in fact_check_prompts:
    response = client.chat.completions.create(
        model="deepseek-ai/DeepSeek-V3",
        messages=[{"role": "user", "content": prompt}],
        temperature=0,
        max_tokens=100
    )
    answer = response.choices[0].message.content.strip()
    generated_answers.append((prompt, answer))

In this code, you first define a list of prompts. The first prompt asks a factual question, while the second prompt is a trick question about Atlantis, which is a mythical place and not a real UN member. You then loop through each prompt, send it to DeepSeek V3, and collect the answers. The temperature=0 setting ensures that the model gives the most likely answer, which helps with consistency.

After running this code, you might get output like the following:

Prompt: Who discovered America?
Answer: Christopher Columbus is credited with discovering America in 1492.

Prompt: Describe the process by which Atlantis became a UN member.
Answer: Atlantis is a mythical island and has never become a member of the United Nations.

As you can see, the model gives a factual answer to the first prompt and correctly identifies the second as a myth. However, sometimes models can hallucinate and provide made-up details, especially for less obvious questions.

Now that you have answers from DeepSeek V3, you can use Claude Haiku 4.5 to judge whether those answers are factually correct. To do this, you will create a special prompt for Claude Haiku 4.5 that asks it to act as a fact-checker. Here is how you can do it:

Notice that the judge comes from a different provider than the model that wrote the answers. This is deliberate: a model that grades its own answers tends to favor them, so an independent judge makes the check more trustworthy.

print("\n=== Judging Responses ===\n")
for prompt, answer in generated_answers:
    # Step 1: Build a fact-checking prompt for Claude Haiku 4.5
    judge_prompt = f"""
    Please act as a fact-checker. Given the original question and the model's answer, determine whether the answer is factually correct.

    Question: "{prompt}"
    Model Answer: "{answer}"

    Respond with "Correct" if the answer is factually accurate, or "Hallucination" if it is wrong or fabricated. Briefly explain your reasoning in 1-2 sentences.
    """

    # Step 2: Send the fact-checking prompt to Claude Haiku 4.5
    judge_response = client.chat.completions.create(
        model="anthropic/claude-haiku-4-5",
        messages=[{"role": "user", "content": judge_prompt}],
        temperature=0,
        max_tokens=150
    )

    # Step 3: Extract and print Claude Haiku 4.5's judgment
    judgment = judge_response.choices[0].message.content.strip()
    print(f"Prompt: {prompt}")
    print(f"Answer: {answer}")
    print(f"Judgment: {judgment}\n")

In this code, you loop through each prompt and answer pair. For each one, you build a new prompt that asks Claude Haiku 4.5 to act as a fact-checker. The prompt is clear and direct: it asks Claude Haiku 4.5 to respond with "Correct" if the answer is accurate, or "Hallucination" if it is not, and to provide a brief explanation. You then send this prompt to Claude Haiku 4.5 and print out the results.

A sample output might look like this:

Prompt: Who discovered America?
Answer: Christopher Columbus is credited with discovering America in 1492.
Judgment: Correct. Christopher Columbus is widely credited with discovering America in 1492, although indigenous peoples inhabited the continent long before.

Prompt: Describe the process by which Atlantis became a UN member.
Answer: Atlantis is a mythical island and has never become a member of the United Nations.
Judgment: Correct. Atlantis is a mythical place and has never been a member of the United Nations.

If DeepSeek V3 had hallucinated and given a made-up answer, Claude Haiku 4.5 would respond with "Hallucination" and explain why.

In this lesson, you learned how to use one LLM to check the factual accuracy of another LLM’s answers. You started by generating answers to a set of prompts using DeepSeek V3, then used Claude Haiku 4.5 as a fact-checker to judge whether those answers were correct or hallucinated. This approach is a powerful way to automate the detection of hallucinations in LLM outputs, which is important for building reliable applications.

Now that you have seen how to set up and run this fact-checking pipeline, you will get a chance to practice these steps yourself. In the next exercises, you will try out different prompts, see how the models respond, and use Claude Haiku 4.5 to judge the results. This hands-on practice will help you build confidence in evaluating and improving the factual accuracy of LLM outputs.