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Getting Started with AI: What You Need to Know


10 min read

Written by


Argos Multilingual

Published on

10 Jun 2024

The idea of using Artificial Intelligence (AI) to make your work easier is a promising idea but it takes preparation in order to get good results. Now that we understand a little more about how Large Language Models (LLMs) are trained and how they operate, as highlighted in this first blog in our AI series, we can appreciate the advantages they offer, including improved efficiency, scalability, and quality.

However, successful implementation requires a clear understanding about what these technologies can — and can’t — do. In this second blog in our AI series, we’ll walk you through a few things to consider before you start, discuss critical security considerations, and explore common challenges and strategies to address them.

Beyond understanding the technology itself, it’s crucial to align AI initiatives with your specific business needs. A common joke making the rounds right now asks why AI is doing creative things for tedious people instead of taking over doing tedious things for creative people.

That’s a broad generalization, but it makes a good point. The smart thing to do with AI is to identify the places where it adds the most value and ensure you have the necessary resources and expertise to support these efforts. By taking a strategic approach, you can maximize the benefits of AI while minimizing potential pitfalls.

To get a more comprehensive view of how companies are navigating AI integration, you can check out Argos Multilingual’s latest research publication, “Beyond the Hype: State of AI in Localization in 2024.” This report offers valuable insights into the real-life experiences of localization managers, providing practical advice and lessons learned from those who are busy getting hands-on experience with the tech.

What to Know Before You Start

As exciting as the potential of AI and LLMs may be, it’s important to approach their integration with a clear understanding of both their capabilities and limitations. Setting realistic expectations upfront will help ensure a smooth and successful implementation.

Initial Setup and Expectations

When it comes to AI, it’s important to avoid getting swept up in the hype. While these technologies offer remarkable potential, they are not a silver bullet. Take the time to carefully assess your specific needs and align them with business goals, and then set your expectations accordingly.

Some key questions include:

  • What are the exact pain points you’re hoping to address with AI?
  • What level of automation are you realistically aiming for?
  • How will you measure the success of your AI implementation?
  • Do you have resources with the right skill sets to support AI initiatives?

Plan to collect data before you test AI, while you’re testing, and afterwards. This data will help when planning next steps and will support your recommendations when presenting to senior leadership.

By establishing clear, achievable objectives from the start, you’ll be better positioned to integrate AI into your workflows in a way that delivers measurable results.

Security Considerations

Another critical factor to weigh is data privacy and security. When using AI to handle sensitive information, such as customer data or proprietary content, robust safeguards are essential. Users should ask questions before entering any personal or business data into an LLM: How secure is my data? How will the LLM use my data? Pausing to understand the repercussions before using these online AI tools could avoid a lot of issues down the road.

Understanding AI Hallucinations

One of the major limitations of LLMs is their tendency to hallucinate or generate plausible sounding but factually incorrect information. This can be particularly problematic in mission-critical applications where accuracy is paramount. To mitigate this issue, it’s important to implement validation checks and human oversight. 

You should also be wary of LLMs’ propensity to overuse certain words or phrases, which can result in repetitive, unnatural-sounding text. Developing strategies to diversify the language used, such as thesaurus-based substitutions, can help address this challenge and train the AI, as can pre-planned queries and ban lists with overused words.

Other Limitations

It’s important to recognize that while AI and LLMs offer remarkable capabilities, they also have limitations. Working around these requires an understanding of AI factors beyond privacy and accuracy. Here are some areas to be alert for:

Bias and fairness: LLM users must be vigilant in identifying and mitigating biases to ensure fairness and inclusivity.

Domain specificity: Challenges may arise when applying LLMs to technical, creative, or regulated content, requiring human involvement to ensure accuracy and compliance.

Contextual understanding: LLMs may struggle with contextual understanding, resulting in misinterpretations or errors, particularly with ambiguous or nuanced language.

Legal and regulatory compliance: Compliance with data protection laws, intellectual property rights, and industry-specific regulations is essential to avoid legal repercussions and maintain trust.

Integration with your tech stack: A current limitation of LLMs is whether they can integrate with the tech stack used by your business. How complex is it to set this up? How well will the LLM perform once it’s connected? 

Prompt engineering: Writing and refining prompts is difficult to scale and can be time consuming. Prompts are not homogenous — two different prompts written by the same user will yield different results, not to mention from different users. 

Practical Steps to Start Using AI

Once you’ve resolved questions around resourcing, expertise, and security, there are a few ways you can get started with AI. We recommend beginning with simple, non-critical use cases that can demonstrate the technology’s potential. This could include translating internal communications, generating content such as basic product descriptions, or assisting with customer service inquiries that don’t require high technical accuracy.

These initial experiments serve multiple goals. Firstly, they allow your team to familiarize themselves with AI tools and workflows in a low-risk environment. Secondly, they provide valuable insights into the strengths and limitations of AI as it applies to your business, helping you refine your approach for more complex use cases down the line.

Once you’ve gained confidence and proficiency with basic AI applications, you can gradually tackle more challenging tasks. This might involve automating parts of your localization workflow or implementing AI-driven quality assurance checks to ensure consistency and accuracy across all translations.

Throughout this journey, it’s essential to maintain a flexible mindset. AI technologies are rapidly evolving, and staying informed about the latest developments and best practices will be crucial for maximizing their impact for your business.

Taking the Proactive Path

Early adoption of AI in localization offers strategic advantages for staying competitive. By embracing AI at an early stage of their localization operation, organizations can enhance operational efficiency, improve translation quality, and streamline processes, setting themselves apart as innovators.

To start, engage with industry experts through webinars, conferences, and online forums to stay informed about AI trends. Experiment with pilot projects to test AI-powered tools in real-world scenarios, evaluating their impact on efficiency and quality. Embrace a culture of continuous learning and experimentation to unlock the full potential of AI and position your organization for success.

The Strategic Advantage of AI in Localization

By proactively embracing AI and LLMs in your localization workflows, you can gain a clear strategic edge. It’s worth the investment of time and resources but it also requires informed decision-making, a willingness to experiment, and a commitment to staying ahead of the curve.

For a deeper understanding of the current trends and future outlook of AI in localization, we highly recommend our “Beyond the Hype: State of AI in Localization in 2024” report. This resource offers valuable insights from industry experts and real-world examples to help guide your AI strategy. We also invite you to explore the Argos Multilingual AI site and contact us to learn more about how to start your AI journey.

Stay tuned for the third instalment in our AI blog series, where we’ll dive deeper into tricky subjects like ethics, risk management, and just how secure the tools we use daily really are.

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