Preparing for the AI Revolution: Best Practices for Generative AI Integration

May 4, 2023

If you read part one of Adastra’s ChatGPT series, you know that while ChatGPT can’t do everything, there are many ways it can assist organizations in gaining efficiency, agility, and scalability. But how should organizations go about deploying it?

Below are some of the best practices to put in place to make sure ChatGPT is used safely and in a way that will produce optimal results for your organization.

Choosing the right use cases

One of the first steps an organization can take before undertaking a project using ChatGPT or other OpenAI services for organizational purposes is to assess what are realistic uses for it.

While there is no doubt that ChatGPT is a very powerful tool, it does best and is most powerful when it is used for what it was intended for.

Adastra can help your organization assess what can and cannot be executed using the tool, and in what ways they can best be executed. Or, we can help you determine alternative solutions to the organizational issue or inefficiency you are trying to solve for.

Securing your data

When using ChatGPT for organizational purposes, security is a crucial factor. While your organization may already have strong security practices in place, using OpenAI presents new challenges that require a refreshed strategy.

To mitigate these risks, it’s important to ensure that appropriate security measures are in place to protect user data, including using secure communication channels and regularly auditing the system for vulnerabilities.

Access control is also an important factor to consider. For example, if an AI model is trained for an executive audience and contains confidential organizational information that shouldn’t be available for non-executive audiences, this could pose a challenge. The model will need to be retrained to support different contexts.

Leveraging quality data

The quality and accuracy of ChatGPT outputs relies on the quality and accuracy of the organizational data being supplied to it.

Leveraging quality data is important to eliminate bias, improve accuracy, relevance and credibility, and ensure your OpenAI service can be trained to generate contextually relevant conversations.

Eliminating bias and using ethical AI

When using ChatGPT or other open AI for decision-making, there are several challenges to account for.

The first is inherent biases in the datasets that were used to train the models, which can lead to discriminatory or unfair decisions.

The lack of transparency and accountability in AI’s decision-making can also make some organizational stakeholders weary and can be a challenge for getting organizational approval.

Lastly, differences in methodology and a lack of standardized decision rules or thresholds can cause organizational tension, which highlights the need for collaboration between domain experts and analytics experts to establish criteria for confidently accepting AI outputs for decision-making.

Adastra’s ethical AI framework uses Human in the Loop (HIL) to create, validate and update AI models, reducing bias and ethical concerns.

Preparing for the AI Revolution: Best Practices for Generative AI Integration

Illustration: Ethical AI Framework 

Leveraging a modern data architecture

A modern architecture allows organizations the scalability, real-time processing, data integration, flexibility and agility to easily supply quality data to ChatGPT.

By leveraging a modern data architecture, combined with Adastra’s expertise, organizations can ensure that ChatGPT is accessing the right data, at the right time in a cost-efficient way, providing more accurate and relevant responses to user requests, ultimately leading to better business outcomes.

Access to technical expertise

All in all, your organization will be able to maximize the benefits of open AI with the help of a seasoned technical expert that understands the foundational large language models fueling ChatGPT and is skilled in interface development and building enterprise applications and visualizations.

Adastra’s experts are able to retrain the backend models to support contextually aware search functionality and produce results specific to your situation and organization.

Reach out to one of our experts to begin unlocking the full potential of ChatGPT.

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