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The appeal of generative artificial intelligence (GenAI) and large-scale language models (LLMs) is undeniable. Especially for entrepreneurs looking to differentiate in an increasingly competitive marketplace, even the slightest technological advantage and data-driven insights can mean the difference between winning or losing a client or customer.
As such, these cutting-edge technologies hold the “keys to the kingdom” in terms of which companies, sectors, industries and nations will win, while other technologies lag behind in their ability to personalize products, innovate experiences, automate tasks and unlock unprecedented levels of creativity and efficiency.
Naturally, the temptation is to jump right in and fine-tune these artificial intelligence (AI) powerhouses and unlock their potential. But this is why entrepreneurs and businesses need to exercise caution.
Think of it this way: you wouldn’t launch a rocket without a guidance system. Jumping into GenAI without robust governance is like launching a product to market blindfolded: it might launch successfully, but as it goes into orbit it will gradually fall apart, and the spacecraft will inevitably explode.
If this rocket goes off course by even half a degree due to margin of error, it will miss its target (be it the Moon or Mars) by millions of miles. And that is exactly what is happening to entrepreneurs and companies trying to “customize ChatGPT as their solution.”
Good governance acts as a navigation system, ensuring AI initiatives move in the right direction and are aligned with values.
It was therefore not surprising that just days after Dubai Crown Prince Sheikh Hamdan bin Mohammed bin Rashid Al Maktoum took the initiative to appoint 22 chief AI officers, he hosted an AI retreat for more than 2,500 government officials, executives and experts to prioritize AI governance in the public sector and set a strong precedent for the private sector as well.
Related: How to create a future where artificial intelligence is a force for good
Limitations of LLMS
To understand the complexities involved, we provide a brief overview of the architecture of LLMs such as OpenAI’s GPT series, which are built using a special type of architecture called a Transformer.
These models are able to understand and generate highly human-like text by looking at the context of entire sentences or paragraphs, not just individual words, and do this using a technique called self-attention.
However, LLMs have some limitations: they can only see and understand things based on the data they are trained on, and their behavior and responses are determined by the methods and goals used when they were developed.
What the model produces can be difficult to control because training data contains biases. Mitigating these biases requires careful data management and ongoing monitoring.
In addition to bias, it is important to note that companies often face challenges related to data quality when deploying large models in their environments and adapting or adding context to their corporate data. Many companies lack proper metadata on their data, which can hinder the effective implementation of AI systems. These factors must be taken into account when defining the architecture and designing a system suitable for enterprise use.
Related: As artificial intelligence booms, startups still need the human touch to succeed
Bias in training data is just the tip of the iceberg, so let’s dig deeper and learn why a governance-first approach is important.
Don’t just build, curate: guardrails are key
Despite its impressive capabilities, LLM is essentially a sophisticated pattern-matching machine. It lacks the nuanced human understanding of context and outcomes. Without proper guardrails, even the most well-intentioned tweaks can lead to output that makes absolutely no sense. Establishing clear boundaries and oversight is critical to prevent unintended damage.
Think of interaction guardrails as building a fence around your AI’s playground. These “fences,” which come in the form of bias detectors, content filters, and security protocols, are essential to prevent AI from straying into dangerous or unethical territory.
Proactive guardrails ensure that AI interacts with the world responsibly, mitigating risks and fostering trust among users.
Facilitating feedback loops
Training an LLM is not a one-and-done exercise: to truly realize its potential, we need to put in place a robust feedback loop, which involves systematically collecting high-quality feedback and model outputs, collaboratively cleaning and labeling data, and running disciplined experimentation on how to fine-tune.
By comparing the results of different tuning approaches, you can continuously optimize your model’s performance. Setting up such a feedback mechanism may require 4-6 weeks of intensive effort, but the payoff in terms of improved LLM capabilities will be immense.
The true potential of GenAI and the LLM lies not in hasty adoption, but in fostering a culture of responsible AI development, which requires a long-term perspective that prioritizes ethical considerations, transparency, and continuous learning.
For an LLM to be truly useful, it needs to adapt to the domain and specific use case it will be used in. This can be achieved through domain-specific pre-training, fine-tuning, search extension generation, and prompt engineering techniques such as few-shot learning and directed prompting.
The choice of approach depends on your specific use case and considerations such as prompt window size, model size, computing resources, and data privacy.
Don’t rush to be first, but strive to be the best. Invest in a robust governance framework, have an open dialogue with stakeholders, and prioritize continuous monitoring and improvement.
Governance should determine who has access to AI systems within a company: for example, a team member should not be able to ask a model about other employees’ salaries and receive that information.
LLM implementation and access should follow or be an extension of existing data governance policies, which often include clearly defined role-based access controls.
After all, slow and steady wins the day, and this is especially true when it comes to AI adoption. Taking a governance-first approach will help you maximize the transformative power of GenAI.
Related: An Entrepreneur’s Guide to Starting an Artificial Intelligence Business in Dubai
