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Building Trustworthy Generative AI Applications

Notes:  For more articles on Generative AI, LLMs, RAG etc, one can visit the “Generative AI Series.” 
For such library of articles on Medium by me, one can check: <GENERATIVE AI READLIST>

In the rapidly evolving AI landscape, the question of trust in generative AI looms large. Companies are concerned about the reliability, accuracy, and security of model outputs. Generative AI’s strength in producing diverse content comes with the risk of hallucinations. Similar to the financial crisis and e-commerce challenges, unchecked Generative AI can lead to misinformation and mistrust. So, there are also ethical and legal considerations.

So, what should be our approach when it comes to building trustworthy Gen AI models?

Trust begins with understanding how foundation models differ fundamentally from their predecessors. Unlike traditional approaches that demanded “more” models and data scientists, foundation models leverage internet-scale data. This transformative shift allows a single model to adapt and fine-tune for diverse use cases, emphasizing less on model quantity and more on fine-tuning and usage.

So domain expertise is must.

Layers of Trust

To harness foundation models effectively, companies must comprehend four critical layers: data, the model itself, prompt requests, and applications. How a company navigates and controls these layers determines its trust approach — whether to buy, boost, or build.

• Buying for Simplicity: Opting for end-to-end services offers speed and simplicity, resembling a software-as-a-service model. However, this convenience sacrifices control and customization. I personally do not support this approach.
• Building for Control: At the opposite end, building a custom model provides total control, though it demands a significant commitment. Companies may start with open source language models (LLM) and add domain data for customization. This is the best approach if the company has significant funds. But for a start a hybrid of simplicity and control is preferred, a middle ground, as explained in next point.
• Boosting for Customization: A middle ground involves boosting an existing foundation model through fine-tuning. This allows immediate customization with fewer examples of domain data, balancing control and simplicity. This is what I usually suggest. It balanced amount of control and simplicity gives us ample freedom to be creative while not focussing on repetitive tasks too much.

Key Business Decisions Influencing Trust

The layers and consumption patterns directly impact key business decisions related to trust:

1. Architectural Choices: Companies must select architectures that ensure relevant, reliable, and usable model outputs. Considerations include managing knowledge graphs, vector databases, and prompt engineering libraries based on the chosen consumption model.
2. Security Measures: Ensuring model security involves addressing IP and data leakage, safeguarding sandboxes, protecting against prompt injection attacks, and mitigating the risk of shadow IT.
3. Responsibility in AI: Given the widespread accessibility of generative AI, responsible AI practices are paramount. Updating guidelines, comprehensive training, bias identification in training data, and vigilance on evolving legal issues ensure responsible AI use.

In conclusion, building trustworthy generative AI necessitates a thoughtful approach to the layers, consumption models, and key business decisions. Striking the right balance between control, customization, and simplicity is crucial.

Ultimately, the central question remains: How do we build generative AI we can trust? Answering this question is pivotal for unlocking the full potential of this transformative technology.

A few pointers that can help are as follows:

To mitigate risks, addressing Generative AI issues during the initial design phase is crucial, preventing trust issues post-deployment.

A structured approach is vital to harness Generative AI potential responsibly. Three points come to my mind.

a. Avoid Chances of Hallucinations from the beginning

1. Start with Robust Training Data: Ensure diverse and comprehensive datasets for reliable outputs
2. Incorporate Feedback Mechanisms: Allow user reporting to refine models and build trust
3. Prompt Engineering for Hallucination Mitigation: Design prompts to guide accurate outputs, reducing hallucination risks.
4. Foundational Principles in AI: Embed ethical guidelines into AI architecture for factual boundaries.
5. Maintain Transparency: Clearly label AI-generated content to uphold user trust.

b. A robust governance is necessary

1. Establish Clear Usage Guidelines: Define boundaries, especially in sensitive areas like news generation or medical advice.
2. Implement Human Oversight: Human review ensures accuracy and relevance.
3. Conduct Regular Audits: Periodic assessments detect issues early.
4. Prioritize Ethical Considerations: Reflect on moral implications, aligning Generative AI use with societal values.

c. Adopting a user friendly design goes a long way

1. Adopt a User-Centric Design: Prioritize the user experience for seamless interaction. They should be a part of creative process. Human feedback is a must.
2. Privacy and Security Design: Implement robust security measures to protect user data.
3. Acknowledge System Limitations: Communicate system limitations transparently.
4. Iterative Design for Continuous Improvement: Design Generative AI systems for continuous evolution based on user feedback.

In conclusion, unlocking the vast potential of Generative AI requires foresight and responsibility. By addressing challenges proactively, building precision, instituting robust governance, and emphasizing thoughtful design, businesses can ensure innovation, efficiency, and user trust in the digital age.