It Takes More than Prompt Engineering to Achieve GenAI Accuracy

Generative AI’s adoption rate is accelerating, thanks to its alluring capabilities across industries and businesses of all sizes.  

According to a recent survey, 65% of respondents confirm that GenAI is regularly used at their respective organizations – almost double the amount reported last year. However, rapid integration of GenAI without a proper strategy for security and use practices can incur significant risks, notably data leaks, biases, inappropriate content, and hallucinations. When these issues occur without robust safeguards, these inherent risks can quickly turn GenAI applications from valuable assets into liabilities that could spark reputational damage or financial losses.

Prompt engineering – the practice of modifying text instructions to steer AI outputs toward desired responses – is a best practice for responsible and safe AI deployment. Nevertheless, GenAI can still inadvertently jeopardize sensitive data and propagate misinformation from the prompts it is given, especially when these prompts are overloaded with details. 

Fortunately, there are several other ways to mitigate the risks inherent in AI usage.   

Engineering Faults 

While prompt engineering can be effective to a point, its drawbacks often outweigh its advantages. 

For one, it can be time-consuming. Constantly updating and fine-tuning prompts to keep pace with the evolving nature of AI-generated content tends to create high levels of ongoing maintenance that are difficult to manage and sustain. 

Though prompt engineering is a common go-to method for software developers looking to ensure natural language processing systems demonstrate model generalization – i.e., the capacity to handle a diverse range of scenarios appropriately – it is sorely insufficient. This approach can often result in an NLP system that exhibits difficulty fully comprehending and accurately replying to user queries that may deviate slightly from the data formats on which it was trained.

Regardless, efficient prompt engineering depends heavily on unanimous agreement among staff, clients, and relevant stakeholders. Conflicting interpretations or expectations of prompt requirements create unnecessary complexity in coordination, causing deployment delays and hindering the end product.

What’s more, not only does prompt engineering fail to completely negate harmful, inaccurate, or nonsensical outputs, but a recent study indicates that, contrary to popular belief, this method may actually be exacerbating the problem. 

Researchers found that the accuracy of an LLM (Large Language Models), which is inherent to AI functioning, decreased when given more prompt details to process. Numerous tests revealed that the more guidelines added to a prompt, the more inconsistently the model behaved and, in turn, the more inaccurate or irrelevant its outputs became. Indeed, GenAI’s unique ability to learn and extrapolate new information is built on variety – overblown constraints diminish this ability. 

Finally, prompt engineering doesn’t abate the threat of prompt injections – inputs hackers craft to intentionally manipulate GenAI responses. These models cannot still discern between benign and malicious instructions without additional safeguards. By carefully constructing malicious prompts, attackers are able to trick AI into producing harmful outputs, potentially leading to misinformation, data leakage, and other security vulnerabilities.

These challenges all make prompt engineering a questionable method for upholding quality standards for AI applications. 

Reinforced Guardrails 

A second approach, known as AI guardrails, offers a far more robust, long-term solution to GenAI’s pitfalls than prompt engineering, allowing for effective and responsible AI deployments. 

Unlike prompt engineering, AI guardrails monitor and control AI outputs in real-time, effectively preventing unwanted behavior, hallucinatory responses, and inadvertent data leakages. Acting as an intermediary layer of oversight between LLMs and GenAI interfaces, these mechanisms operate with sub-second latency. This means they are able to provide a low-maintenance and high-efficiency solution to prevent both unintentional and user-manipulated data leakages, as well as filtering out falsehoods or inappropriate responses before they reach the end user. As AI guardrails do this, they simultaneously render custom policies that ensure only credible information is ultimately conveyed in GenAI outputs. 

By establishing clear, predefined policies, AI guardrails ensure that AI interactions consistently align with company values and objectives. Unlike prompt engineering, these tools don’t require security teams to adjust the prompt guidelines nearly as frequently. Instead, they can let their guardrails take the wheel and focus on more important tasks.

Furthermore, AI guardrails can be easily tailored on a case-by-case basis to ensure any business can meet their respective industry’s AI safety and reliability requirements. 

Generative AI Needs to be More Than Just Fast – It Needs to be Accurate.

Users need to trust that the responses it generates are reliable. Anything less can spell enormous negative consequences for businesses that invest heavily into testing and deploying their own use case-specific GenAI applications. 

Though not without its merits, prompt engineering can quickly turn into prompt overloading, feeding right into the pervasive security and misinformation risks to which GenAI is inherently susceptible.  

Guardrails, on the other hand, offer a mechanism for ensuring safe and compliant AI deployment, providing real-time monitoring and customizable policies tailored to the unique needs of each business. 

This shift in methodology can grant organizations a competitive edge, bolstering the stakeholder trust and compliance they need to thrive in an ever-growing AI-driven landscape.

About the Author

Liran Hason is the Co-Founder and CEO of Aporia, the leading AI Control Platform, trusted by Fortune 500 companies and industry leaders worldwide to ensure trust in GenAI. Aporia was also recognized as a Technology Pioneer by the World Economic Forum. Prior to founding Aporia, Liran was an ML Architect at Adallom (acquired by Microsoft), and later an investor at Vertex Ventures. Liran founded Aporia after seeing first-hand the effects of AI without guardrails. In 2022, Forbes named Aporia as the “Next Billion-Dollar Company”.

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