In AI, “hallucination” refers to instances where Large Language Models (LLMs) generate information that is incorrect, nonsensical, or fabricated, despite presenting it with confidence. These models do not intentionally lie but produce outputs that are not grounded in factual data. This phenomenon occurs because LLMs are fundamentally designed as next-word predictors, aiming to generate coherent text based on patterns in their training data. While this approach often produces plausible results, it can also lead to statistically convincing but factually incorrect outputs.

Hallucinations can be relatively harmless in casual chatbot interactions but pose significant risks in Agentic AI systems. In such systems, where the AI is goal-oriented and tasked with creating detailed workflows to achieve objectives, a single hallucinated step—such as an incorrect API call or a fabricated fact—can silently derail the entire process. This is particularly concerning in high-stakes industries like healthcare, finance, and law, where the consequences of hallucinations can be severe or even life-threatening.

To mitigate hallucinations, several strategies can be employed in the design and deployment of LLMs, especially in Agentic AI systems.

Retrieval-Augmented Generation (RAG)

Retrieval-Augmented Generation (RAG) is a powerful technique to reduce hallucinations in LLMs. This method integrates the model with a trusted knowledge base or external data source. When the model processes a query, it retrieves relevant information from the knowledge base and uses it to generate a response. This ensures that the output is anchored in factual data rather than speculative guesses.

If the knowledge base lacks the necessary information, the model should be designed to avoid fabricating answers. Instead, it can provide a fallback response, such as stating that it does not have sufficient information to answer the query. This approach not only prevents the generation of misleading content but also reinforces user trust by ensuring that the model only responds based on verified data.

Tool Calling

Another effective strategy to minimize hallucinations is enabling the AI model to interact with real-world APIs. By querying APIs, the model can fetch accurate and up-to-date information directly from trusted sources. For instance, when providing weather updates, stock prices, or other dynamic data, the model can rely on API calls to ensure precision and reliability. This approach reduces the risk of speculative or incorrect outputs and enhances the model’s credibility.

Tool calling is particularly useful in scenarios where real-time accuracy is critical. By integrating API interactions into the model’s workflow, developers can ensure that the AI’s responses are grounded in current and verifiable data, significantly reducing the likelihood of hallucinations.

Grounding Responses

Grounding the model’s responses in verifiable sources is another critical approach to addressing hallucinations. This involves ensuring that the AI provides citations or references for the information it generates. By linking outputs to authoritative sources, users can trace the origin of the information and verify its accuracy.

For example, when generating responses in domains like healthcare or law, the model can include references to scientific papers, official guidelines, or other reliable documents. This not only enhances the transparency and trustworthiness of the AI but also empowers users to validate the information, making it particularly valuable in high-stakes applications.

Guardrails and Validators

Implementing guardrails and validators is essential for flagging and mitigating hallucinations. Rule-based checks can be used to identify nonsensical outputs, such as fabricated citations or inconsistent information. For instance, if the model references a source, validators can verify whether the citation corresponds to a real and credible document.

Fallback strategies can also be employed when the model’s confidence in its response is low. In such cases, the AI can defer to an actual human to review or provide a guidance where it is required. Additionally, incorporating human-in-the-loop review processes for critical tasks ensures that outputs are thoroughly vetted before being acted upon, further reducing the risk of errors.

By combining these strategies—RAG, tool calling, grounding responses, and implementing guardrails—developers can significantly reduce hallucinations in LLMs, making them more reliable and trustworthy, especially in applications where accuracy is paramount.

As a practice, ALWAYS cross-verify the model responses with the ground truth. That should take care most of the hallucination problem, and additionally you can put extra validations, verifications, guardrails, metrics in place to make it more robust.