7 Practical Ways to Prevent LLM Hallucinations and Protect Your Product

LLM hallucination can murder a product before it is born. LLM hallucinations occur when an AI model produces inaccurate outputs with confidence, even though they aren't supported by its training data. These errors can range from minor inaccuracies to outright false information. For practical LLM applications, especially in domains like healthcare, finance and legal, highly relevant responses are critically important.

At Roro Solutions, we’ve experienced the challenges of LLM hallucinations firsthand, and we’ve also found ways to manage them effectively. In this post, we’ll explore seven methods to control hallucinations, ranked by increasing levels of efficiency and effort.

1. Temperature Control

The temperature setting in LLMs affects how creative or cautious the AI is in generating responses. The parameter ranges from 0 (more deterministic) to 1 (more random). For use cases where accuracy is critical, such as in healthcare or legal scenarios, keeping the temperature low—around 0 or 0.1—produces more reliable outputs.

2. Constraining Prompt

To minimize hallucinations, instruct the LLM to respond only if it has a high or at least moderate degree of confidence. If it’s uncertain, it should simply respond with "I don’t know the answer." This approach helps prevent the model from filling gaps with unreliable information.

3. Reflection Prompt

You can improve accuracy by having the LLM review its own response. After the model generates an answer, submit both the query and the response to another LLM call, asking it to critically assess whether the response is grounded in facts and relevant to the original query.

4. Tool Usage

For highly specialized use cases involving proprietary information or logic, you can integrate tools that the LLM can access through API calls or custom algorithms. Tools can be implemented either through direct tool-calling (offered by some LLM vendors) or by using agents like LangGraph to execute complex tasks.

5. RAG

One of the most popular methods, RAG is particularly useful when you need the model to base its response on specific knowledge that it wasn’t trained on. With RAG, the LLM generates responses using information retrieved from external documents or databases, ensuring more relevant and accurate output.

6. Corrective RAG

While basic RAG is powerful, it has its own risks, particularly if the retrieved documents are not highly relevant. Corrective RAG adds an extra layer of protection by having the LLM check the relevance of the retrieved context before using it to generate a response. Irrelevant documents are filtered out, reducing the chance of hallucinations.

7. Finetuning

Fine-tuning involves training the model with an instruction dataset that teaches it to refrain from answering when uncertain. This method can significantly reduce hallucinations by encouraging the model to ask for more information or critique its own ability to answer. While highly effective, fine-tuning can be time-consuming and costly, as it requires adjusting the deeper layers of the neural network responsible for generating responses.

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These methods are not mutually exclusive—you can combine them to suit your specific needs. As you decide which strategies to use, several factors should guide your choice:

• The value and criticality of the output
• Project timelines
• Budget constraints
• Response time requirements
• Whether you need streaming output

We will continue to share more on these factors in our upcoming blog posts. In the meantime, if you’re grappling with LLM challenges, let’s connect! We’d love to help you tailor solutions that meet your unique project needs. Reach out to discuss your LLM problem statements with us today!