Safeguarding generative AI deployments: How to avoid PR challenges

February 9, 2024

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Recent headlines have brought generative AI further into the spotlight. Rogue generative bots are a clear example of both the complexities and possibilities within the field of artificial intelligence and the importance of careful deployment.

In this blog, we’ll explore the essential aspects of safeguarding generative AI deployments to avoid potential PR challenges.

The importance of safeguarding generative AI

The viral DPD incident highlights the risks of implementing generative AI in customer support without adequate safeguards. While building AI assistants is an exciting opportunity for businesses to innovate, it’s essential to recognize the work required to deploy something safely.

Generative AI offers powerful capabilities across various applications. However, the challenge is managing and directing these capabilities effectively.

Without a comprehensive system in place, rogue behaviors occur, where seemingly harmless prompt injections lead to undesirable outcomes that can have a negative, long-lasting impact on brand reputation.

RAG: Striking the right balance

Retrieval Augmented Generation (RAG) helps organizations find a balance between the potential of generative AI and the need for controlled responses.

RAG combines pre-trained language models and a retrieval system to give context-aware answers.

This technique ensures that bots check their generated responses against a knowledge base. RAG acts as a safeguard, preventing inaccurate, irrelevant, and inappropriate responses, and keeps customer conversations within set limits.

Here’s an example of RAG working effectively for a PolyAI customer:

Build or buy: Navigating the DIY dilemma

The eternal debate between the internal development of generative AI bots and partnering with established AI companies becomes crucial when considering AI safeguards.

The DIY approach to AI safeguarding is deeply complex. While internal development may bring a level of control, it requires massive investment in expertise, testing, and ongoing maintenance.

Building adequate safeguards requires a deep understanding of machine learning, extensive testing, and awareness of potential risks. The DPD scenario highlights the risks of embarking on a DIY journey without the necessary expertise and exhaustive testing.

Collaborating with proven AI companies ensures accountability, expertise, and a track record of successful generative AI safeguarding.

Conclusion

Safeguarding generative AI deployments is essential in avoiding potential PR challenges. Recent headline incidents highlight the risks of inadequate measures in implementing AI, emphasizing the need for careful deployment.

Ultimately, the successful deployment of generative bots comes from a thorough assessment of resources, expertise, and long-term goals when navigating the complexities of generative AI.


In this episode of Deep Learning with PolyAI, we explore the necessity of safeguarding when deploying generative AI applications.

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