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Do’s and don’ts for navigating custom GPTs

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Custom GPTs are a powerful tool when developed and used correctly. Here are some considerations to keep in mind.

ChatGPT can tell you the distance between planets, write a sonnet about a lost love, and craft a good knock-knock joke. But ask it to go deep on an industry topic, and its vast yet shallow knowledge often comes up short.

Businesses need a different solution, something that has a deep understanding of the nuances of their specific industry.

Custom GPTs are the answer. These AI models are trained on specific datasets that allow them to speak the language of your business. But like any powerful tool, custom GPTs require careful consideration to maximize their benefits and mitigate risks. 

What are custom GPTs?

Custom Generative Pre-trained Transformers (GPTs) are specialized AI models tailored for specific uses. They understand and generate human-like text.

Unlike standard GPT models, which provide generalized responses based on a diverse dataset, custom GPTs are trained on tailored data. This means they can perform highly specialized tasks with greater accuracy and relevance.

To build a custom GPT, start with a basic AI model. Then train it on data that matters to your field. This may include your industry’s jargon, document formats, and common questions.

For instance, a custom GPT for a financial institution might be trained exclusively on economic reports, financial news, and market trends. Unlike an all-purpose large language model (LLM), this specialized training enables the GPT to provide highly relevant insights and analyses.

There are lots of great sources for prebuilt custom GPTs, including custom chat GPTs. For a list of the best custom GPTs, check out gpt.ai.

Do’s and don’ts for custom GPTs

Custom GPTs are a powerful tool when developed and used correctly. But they can also easily veer off course. Here are some considerations to keep in mind.

Do: Tailor to specific needs

Custom GPTs excel in narrow use cases. Take education, for instance. A custom GPT trained on a university’s course catalog can support students through chatbots that help with course choices and admissions.

However, custom GPTs are not ideal for general-purpose uses. Their specialized nature means they might not perform well in scenarios outside their training.

Don’t: Overlook training data quality

An AI model is only as good as the data it’s trained on. Overlooking data quality and fast-forwarding to use cases will guarantee underperformance.

Custom GPTs work best when they’re fed plenty of relevant, meticulously vetted data. Doing so reduces the risk of producing inaccurate or misleading information. The higher the quality of the training data, the stronger the outputs will be.

Do: Explore general AI options first

Before developing a custom GPT, take a step back and evaluate whether your task can be accomplished with a general-purpose AI.

Custom GPTs excel at complex tasks with rich data sets, but for simpler tasks, traditional AI approaches might be more efficient and cost-effective.

Also, consider the resources required to develop and maintain a custom solution. For certain applications, a prebuilt AI solution might deliver the desired results without the need for extensive customization.

Related: Is your business AI-ready?

Don’t: Use these tools to replace employees

Company leaders should not think of these tools as employee replacements. Rather, custom GPTs should act as a complement to your employees, freeing up their time and mental energy for higher-value work.

Here are some of the best custom GPT use cases that complement human capabilities:

  • Content creators can develop a custom GPT to stay updated on SEO best practices, allowing them to focus more on creative work.
  • Lawyers can use a custom GPT to help them analyze legal documents so they can spend more time crafting strategic arguments.
  • Customer service representatives can delegate basic questions to a custom GPT, allowing them to handle more complex customer interactions.

Don’t: Underestimate implementation challenges

Custom GPTs hold immense potential, but it’s important to be realistic about the implementation challenges involved. Don’t underestimate the hurdles you might encounter when integrating these AI solutions into your existing workflows.

Here are some key areas to consider:

Legacy system compatibility

Legacy systems may require upgrades or modifications to ensure smooth data flow between the AI and your existing tools.

Scalability considerations

Carefully assess your growth projections. Ensure that your custom GPT can handle increased data volumes and user interactions as your business expands.

Change management and training

Investing in training programs is crucial to ensure your team understands how to effectively manage and interpret AI outputs. This might involve teaching them how to identify potential biases in the AI’s responses, as well as how to leverage the AI’s capabilities to augment their own skillsets.

Read also: The top 3 metrics of generative AI in business

Do: Implement rigorous data security measures

Building trust in AI systems requires a strong foundation in data security. To achieve this, meticulous tracking and control over data movement is essential. Knowing where your data goes, who has access, and for what purpose is critical.

Tools like data lineage tracking can help map data flows and identify potential vulnerabilities.

A layered approach to access control is also crucial. Granting permissions based on the principle of least privilege and regularly reviewing access ensures that only authorized personnel can touch sensitive data.

Don’t: Neglect testing and restrictions

Neglecting proper testing and restrictions on custom GPTs can lead to unintended consequences.

Before full integration, rigorous testing that encompasses functionality, security, and potential biases within the model is vital. This may uncover issues that could result in data leaks or inaccurate outputs.

Establishing clear and well-defined guidelines on what type of data custom GPTs can access also helps prevent misuse.

Do: Enhance accessibility

Custom GPTs can be a game-changer for breaking down language barriers.

Their impressive multilingual capabilities allow you to provide customer service, educational resources, or marketing materials in multiple languages. This fosters inclusivity and lets you effectively tap into new markets and demographics.

But accessibility goes beyond language. Custom GPTs can also be equipped with accessibility features for users with visual or auditory impairments. Text-to-speech functionality can convert written content into spoken language, while closed captions can be generated for video content.

Do: Consider ethical implications

Responsible use of custom GPTs demands careful consideration of ethical implications. These AI systems are trained on vast amounts of data, which can unintentionally encode societal biases that can lead to unfair or discriminatory outputs.

To mitigate this risk, regular audits for bias are essential. This involves proactively testing the AI for potential biases in its responses and taking corrective actions to ensure fairness and inclusivity.

Transparency is another key aspect of ethical AI use. Understanding how custom GPTs arrive at their decisions allows for human intervention when necessary. This can involve providing users with explanations for the AI’s recommendations or outputs.

Finally, safeguards against misuse are crucial. Clearly defined guidelines on how custom GPTs can be used help prevent malicious actors from exploiting these AI systems for harmful purposes.

Navigate custom GPTs with care

Custom GPTs offer transformative potential for businesses. By following these dos and don’ts, organizations can navigate the complexities of these tools and use them responsibly and effectively.

But to harness the full power of custom GPTs, you need a strategic approach tailored to your unique needs.

Contact Lumenalta today to hear how we can help you develop and implement custom GPT solutions that unlock new possibilities for your business.

Read next: 5 best practices for effective data product management

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