What Came First: The GenAI or the Guardrails?
Artificial Intelligence is increasingly becoming a part of our everyday lives.
Liran Hason, CEO of Aporia, recently pointed out that we are closer than ever to AI significantly influencing various aspects of our routines.
While this doesn’t necessarily mean widespread job displacement, AI is set to revolutionize how we accelerate and automate tasks in ways we never imagined.
But, as we have already seen, AI makes mistakes.
And these mistakes can be completely unforgiving, making it crucial to have effective mechanisms to manage and mitigate them.
This is where the concept of AI guardrails comes into play.
Understanding Guardrails
AI guardrails are mechanisms and frameworks designed to ensure that AI systems operate within ethical, legal, and technical boundaries.
They prevent AI from causing harm, making biased decisions, or being misused.
Think of them as safety features, similar to your car's lane-assist system or highway guardrails, which keep you on the right path.
Guardrails can be integrated into the AI system itself (like instructions embedded in the system prompt) or applied externally through third-party solutions, akin to road safety barriers.
These instructions - such as ‘avoid offensive language’ or ‘keep the conversation geared towards X at all times’, - are crucial for guiding AI behavior and ensuring it functions safely and effectively.
What Came First: The GenAI or the Guardrails?
As we have established, guardrails can take the form of guidelines or instructions written directly into the system prompt.
Therefore, by definition, guardrails preceded GenAI systems.
Even the earliest AI models, such as Hidden Markov Models (HMMs) and Gaussian Mixture Models (GMMs), were not just theoretical constructs but designed with specific constraints and guidelines.
These early models were developed with certain instructions and rules to ensure they could effectively process data and perform tasks like speech recognition.
Thus, guardrails were foundational from the start, guiding the models’ operations and ensuring their functionality.
In essence, the core of any AI application is its ability to function within defined parameters. Guardrails are an indispensable component of this framework.
Without them, AI systems would lack direction and purpose, demonstrating that guardrails have always been a foundational element, even before the emergence of sophisticated GenAI systems.
Did You Get it Right?
Historically, prompt engineering has been the most common form for establishing these guidelines.
However, this process is often time-consuming and challenging for engineers who might prefer focusing on more engaging tasks.
Plus, it has been proven that increasing the number of tokens in the prompt, decreases the accuracy of the LLM.
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And this is what Aporia has set out to change. Aporia provides third-party guardrails that are both custom and OOTB, and don’t mess with any of the system prompt.
With performance surpassing both GPT and NVIDIA/NeMo guardrails in terms of accuracy and latency, Aporia ensures your AI app is safeguarded instantly, without the need for extensive prompt engineering.
So, now that you know guardrails came before GenAI, what are your thoughts? Was this what you expected?
Let us know in the comments!
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