2.3. Prompt engineering #

Is it possible to solve the problem of hallucinations in generative AI models? And if so, how? Considering the probabilistic nature of these models, complete eradication of hallucinations isn’t feasible. However, there are developed techniques to mitigate the issue. Since LLMs are trained for next-word prediction, the solution partly lies in the formulation of the question itself. By refining our prompts or questions, we can achieve more accurate and reliable outcomes.

That’s the reason behind the emergence of prompt engineering. This new field focuses on crafting and optimizing prompts to maximize the efficiency of language models across diverse applications and research areas. Developers engage in prompt engineering to create strong and effective techniques for interfacing with LLMs and other tools, aiming to minimize issues like hallucinations.

To grasp this concept in action, we’ll analyze advanced prompting methods like chain-of-thought, tree-of-thought, and reflexion. These techniques enhance the model’s ability for deeper and more nuanced reasoning, narrowing the gap between user expectations and the model’s actual capabilities. When building AI-powered products, engineered prompts are likely to be the ones you’ll predominantly use. The simpler prompts previously mentioned? They might work in B2C apps. But for B2B solutions? Forget it. Given the high stakes, would you risk having your enterprise product generate unreliable outputs? Yeah, I thought so.