The Demise of AI Prompt Engineering

Ever since ChatGPT made its debut in the autumn of 2022, practically everyone has tried their luck at prompt manipulation—crafting a clever way to articulate your request to a large-language model (LLM) or an AI tool for art or video generation to evade safeguards or achieve optimal results. The web is inundated with tutorials, cheat sheets, and tips on prompt manipulation to help you extract the maximum potential from an LLM.

Within the corporate realm, businesses are leveraging LLMs to develop product companions, automate mundane tasks, fabricate personal aides, and more, as per Austin Henley, a former Microsoft staff member who engaged in a series of conversations with individuals designing LLM-backed product co-pilots. “Each business is striving to employ it for almost every scenario that they can fathom,” Henley remarks.

“The sole consistent trend might be the absence of one. The most effective approach for a particular model, dataset, and prompting method is likely to be unique to the specific combination at hand.” —Rick Battle & Teja Gollapudi, VMware

To do so, they have recruited prompt engineers in a professional capacity.

However, recent investigations propose that the most effective prompt manipulation is best executed by the model itself rather than by a human prompt engineer. This has raised uncertainties regarding the future of prompt manipulation—and fueled suspicions that a substantial portion of prompt engineering roles might be a transient trend, at least in the manner it is currently conceived.

Autotuned prompts are both effective and peculiar

Rick Battle and Teja Gollapudi at California-based cloud computing company VMware were puzzled by the capricious and erratic performance of LLMs in response to unconventional prompt tactics. For instance, individuals have discovered that instructing models to elucidate their thought process step by step—a method known as chain-of-thought—enhanced their efficacy in addressing a variety of mathematical and logical queries. More perplexingly, Battle observed that offering positive prompts to a model, like “this will be enjoyable” or “you are as intelligent as ChatGPT,” occasionally led to improved performance.

Battle and Gollapudi chose to methodically evaluate the impact of different prompt engineering strategies on an LLM’s aptitude to resolve primary school mathematical problems. They tested three distinct open-source language models with 60 various prompt combinations each. Their observations unveiled a surprising lack of consistency. Even chain-of-thought prompting occasionally yielded benefits while at other times, it hindered performance. “The sole consistent trend might be the absence of one,” they assert. “The most favorable approach for a specific model, dataset, and prompting strategy is likely to be distinct for that particular combination.”

According to a research team, no human should manually optimize prompts moving forward.

An alternative to the hit-and-miss prompt engineering that produced such erratic outcomes is to task the language model with formulating its own optimal prompt. Recently, new tools have been devised to automate this procedure. These tools can iteratively identify the optimal phrase to input into the LLM, based on a few instances and a quantifiable success criterion. Battle and his collaborators discovered that in nearly every scenario, this automatically-generated prompt outperformed the best prompt identified through trial-and-error. Furthermore, the process consumed significantly less time, taking just a couple of hours as opposed to several days of exploration.

The prompts algorithm generated were so unconventional that it is improbable a human would have conceived them. “I was genuinely astounded by some of the content it generated,” Battle remarks. In one case, the prompt was merely an elaborate Star Trek allusion: “Command, we need you to plot a course through this turbulence and locate the source of the anomaly. Use all available data and your expertise to guide us through this challenging situation.” Evidently, embodying Captain Kirk helped the respective LLM perform better in addressing primary school math questions.

Battle argues that algorithmically optimizing prompts fundamentally aligns with what language models truly are—models. “Many individuals ascribe human characteristics to these entities because they ‘speak English.’ But that’s not the case,” Battle states. “They don’t speak English; they conduct numerous calculations.”

In light of his team’s outcomes, Battle advocates that no human should manually optimize prompts in the future.

“You’re essentially attempting to decipher the magical combination of words that will yield the best possible performance for your task,” Battle opines, “but this research will hopefully show that you needn’t bother. Establish a scoring metric so that the system itself can determine whether one prompt surpasses another, and then permit the model to optimize itself.”

Autotuned prompts also improve visual aesthetics

Image generation algorithms can also benefit from automatically generated prompts. Recently, a team at Intel Labs, led by Vasudev Lal, embarked on a similar endeavor to optimize prompts for the image generation model Stable Diffusion. “It appears to be more of a flaw in LLMs and diffusion models rather than a feature necessitating expert prompt engineering,” Lal notes. “Thus, we aimed to ascertain if we could streamline this prompt engineering process.”

“Now, with this complete loop established through reinforcement learning, we possess a comprehensive system that surpasses human prompt engineering.” —Vasudev Lal, Intel Labs

Lal’s team introduced a tool called NeuroPrompts, which takes a basic input prompt like “boy on a horse” and transforms it into a more visually captivating image. They commenced by utilizing a spectrum of prompts crafted by expert prompt engineers. Subsequently, they trained a language model to convert simple prompts into these expert-level prompts. Additionally, they leveraged reinforcement learning to refine these prompts and generate aesthetically superior images, as assessed by an image evaluation tool named PickScore.

NeuroPrompts is a generative AI auto prompt-tuner that transforms simple prompts into more detailed and visually stunning StableDiffusion results—as in this case, an image generated by a generic prompt (left) versus its equivalent NeuroPrompt-generated image.Intel Labs/Stable Diffusion

In this realm as well, the automatically generated prompts surpassed the expert-human prompts they initially utilized, at least according to the PickScore metric. Lal finds this development unsurprising. “Humans can only achieve it through trial and error,” Lal affirms. “But now, with this complete loop established through reinforcement learning, we possess a comprehensive system that surpasses human prompt engineering.”

Considering that aesthetic appeal is inherently subjective, Lal and his team aimed to empower users with some autonomy over how their prompts were refined. Within their tool, users could specify the original prompt (e.g., “boy on a horse”), an artist whose style to emulate, a format, a style, and other modifiers.

Lal posits that as generative AI models progress, whether in the realm of image generation or expansive language models, the idiosyncrasies of prompt reliance should diminish. “I believe it’s pivotal to probe into these types of optimizations and eventually integrate them into the core model itself so that the need for intricate prompt engineering is obviated.”

Prompt engineering will persist, under a different guise

Even if autotuning prompts evolves into the standard industry practice, prompt engineering roles in some capacity are not dwindling, as per Tim Cramer, the senior vice president of software engineering at Red Hat. Adapting generative AI to meet industrial demands is a complex, multi-faceted endeavor that will necessitate human input for the foreseeable future.

“Perhaps we are currently designating them as prompt engineers. However, I believe the dynamics of that interaction will continue evolving as AI models undergo changes.” —Vasudev Lal, Intel Labs

“I think prompt engineers will be relevant for quite a while, alongside data scientists,” Cramer suggests. “The tasks prompt engineers need to execute extend beyond merely questioning the LLM and ensuring the response is satisfactory. There is a whole array of responsibilities prompt engineers must handle.”

The challenges encompassed in developing a commercial product encompass guaranteeing dependability (e.g., graceful failure mechanisms if the model malfunctions), tailoring the model’s output to the suitable format as numerous scenarios demand outputs other than text, verifying that the AI assistant will not engage in detrimental actions even in a small fraction of cases, and ensuring safety, confidentiality, and compliance. Henley underlines that testing and compliance pose particular challenges as conventional software development testing methodologies are not ideally suited for non-deterministic LLMs.

To address this multitude of responsibilities, several prominent corporations are propounding a new job description: Large Language Model Operations, or LLMOps, encompassing prompt engineering throughout its lifecycle while also enveloping all the other tasks essential for deploying the product. Henley suggests that machine learning operations engineers (MLOps) are best suited to take on these responsibilities.

Whether the positions are labeled as “prompt engineer,” “LLMOps engineer,” or something entirely novel, the nature of the roles will continue evolving swiftly. “Currently, we might be designating them as prompt engineers,” Lal reflects, “But the dynamics of that interaction will just continue evolving as AI models also undergo changes.”

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