How can prompt engineering help mitigate hallucinations? (E.g., telling the LLM “if the information is not in the provided text, say you don’t know.”)

Prompt engineering helps reduce hallucinations in large language models (LLMs) by explicitly guiding their responses through clear, structured instructions. Hallucinations occur when models generate plausible-sounding but incorrect or fabricated information, often due to gaps in their training data or overconfidence in patterns. By designing prompts that set boundaries and prioritize accuracy, developers can steer models toward reliable outputs. For example, adding a directive like “If the information is not in the provided text, say you don’t know” forces the model to anchor its responses to the input data, reducing the risk of inventing details. This approach works because LLMs rely heavily on context clues in the prompt—clear constraints help them avoid extrapolating beyond what’s provided.

A practical application of this is in question-answering systems. Suppose a developer builds a customer support chatbot that references a specific knowledge base. Without explicit instructions, the model might confidently answer questions about products not mentioned in the knowledge base, leading to misinformation. By adding a prompt like “Answer only using the product descriptions below. If the answer isn’t there, reply ‘I don’t have that information’”, the model becomes more likely to stick to the source material. Similarly, in summarization tasks, prompts such as “Summarize the key points from the article, excluding any details not explicitly stated” can prevent the model from inserting unsupported claims. Developers can also combine these instructions with technical parameters, like lowering the model’s “temperature” setting to reduce creative but risky outputs.

However, prompt engineering isn’t foolproof. Models might still hallucinate if the input data is ambiguous or if the instructions are too vague. For instance, a poorly phrased prompt like “Answer the question” gives the model free rein to guess. To address this, developers should test prompts iteratively, refining them to cover edge cases (e.g., “If the user asks about topics outside the document, list the document’s relevant sections instead of answering”). Pairing prompt engineering with retrieval-augmented generation (RAG) systems—which fetch verified data before generating a response—adds another layer of safety. Ultimately, the goal is to create a feedback loop where prompts, model settings, and external data work together to minimize unreliable outputs while maintaining usability.