Large language models (LLMs) like ChatGPT, Gemini, or LLaMA can already write, translate, and summarize text fluently. However, one problem still plagues this technology: LLMs can produce answers that sound convincing but are actually incorrect, biased, or irrelevant to the context of the question. In high-stakes fields like healthcare, law, or engineering, these kinds of errors are not only annoying but can be dangerous.
Fatawi et al. (2026) attempted to address this issue in their research. The results confirm that LLMs can be significantly more accurate and ethical not by changing the model, but by changing the way we phrase questions.
Large language models (LLMs) have transformed natural language processing (NLP), demonstrating exceptional proficiency in tasks such as text generation, translation, and summarization. However, LLMs are prone to generating biased, inaccurate, or contextually irrelevant outputs, posing significant risks in high-stakes domains such as healthcare, legal reasoning, and engineering. This paper systematically investigates the role of prompt engineering as a solution to these challenges. By strategically designing inputs, prompt engineering enhances LLM performance, yielding more accurate, contextually relevant, and ethically aligned outputs. Advanced techniques, including chain-of-thought (CoT) prompting and retrieval augmented generation (RAG), are examined for their ability to improve reasoning capabilities, reduce errors, and mitigate bias. CoT prompting facilitates structured, stepwise reasoning, while RAG incorporates real-time data, ensuring output accuracy in rapidly evolving fields. In addition, we present a novel comparative perspective on these techniques, highlighting their distinct strengths and limitations across specialized applications such as healthcare diagnostics and scientific data extraction. The findings demonstrate that sophisticated prompt engineering significantly elevates the reliability and precision of LLM outputs, while addressing critical ethical concerns such as data privacy, bias, and hallucination. These insights underscore the necessity of advanced prompt design in optimizing LLMs for high-impact applications, ensuring both performance and ethical integrity.
Prompt Engineering: Not Just a “Trick” of Asking Questions
Prompt engineering is often underestimated, thought to be simply a matter of adding “please explain in detail” to the end of a question. However, according to this study, prompt engineering is a distinct discipline, a strategy for systematically designing inputs so that LLMs produce outputs that are more accurate, contextual, and ethically aligned.
From this point, the research highlights two key techniques that have been most influential. First, Chain-of-Thought (CoT) Prompting. Instead of requiring LLM to immediately provide a final answer, this technique encourages the model to “think” incrementally, step by step, before reaching a conclusion. As a result, the model’s reasoning becomes more structured and easier to trace if errors occur.
Second, Retrieval-Augmented Generation (RAG). This technique injects relevant and up-to-date external data into the answer generation process.
This is especially beneficial for rapidly changing topics, where the model’s inherent knowledge may become outdated. This study then compares the strengths and limitations of each technique, particularly in specific applications such as health diagnostics and scientific data extraction, two domains that demand high precision and zero tolerance for AI hallucinations.
Not Just About Accuracy, But Also Ethics
What makes this study interesting is that the authors don’t stop at “how to make LLM smarter,” but also highlight its ethical aspects: data privacy, bias mitigation, and hallucination reduction. Well-designed prompts, they found, not only improve technical performance but also help maintain the ethical integrity of AI output, something that is increasingly important as LLMs are used for decision-making that have a real impact on humans.
Why Is This Important Now?
Amidst the wave of adoption of generative AI across industries, from customer service to scientific research, research like this serves as a reminder that the quality of AI output doesn’t solely depend on the size or sophistication of the model. How we interact with it is equally important. Prompt engineering, in other words, is a skill that will become increasingly valuable, not just for AI developers, but for anyone who uses LLMs in their daily work.
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http://doi.org/10.11591/ijai.v15.i2.pp1071-1086