AI Guardrails - How to build safe generative solutions for engineering teams

In the 20th century, cars changed how we live. Now, artificial intelligence (AI) is doing the same. Large language models (LLMs) are vital for businesses. But like cars needing rules, AI needs guardrails to avoid problems.

Why do we need to think about guardrails?

Traditional AI uses rules, but LLMs learn in real time, making them crucial for enterprise knowledge systems. They power chatbots and translators, but their probabilistic nature can cause bias and accuracy issues. So, choosing the right LLM for a business problem is essential. Questions about safety, bias, correctness, brand reflection, and legal compliance are key.

Without guardrails, LLMs risk problems for businesses:

• Sensitive Data: Businesses have private data, and LLMs need guardrails to prevent misuse.
• Compliance: Laws are strict. How are you ensuring the laws are followed by local and regional laws?
• Reputation and Trust: Mistakes in AI use can damage a company's reputation. Guardrails prevent this.
• Ethical Concerns: People worry about AI. Guardrails limit AI in areas needing human values.

Picking an LLM for business needs care. Ten questions can help: We must ask the below questions on day one - 

1. Safety and Security: How is data protected?
2. LLM Output Compliance: How is bias reduced? Are there standards for content?
3. Will you use my data to train my model?
4. How it may impact my and my customer's data?
5. How is bias handled?
6. How is factual accuracy ensured?
7. Is there a detailed evaluation process?
8. What is the data retention compliance once we decide not to use it after a duration?
9. Brand Safety - How do you ensure, the voice matches our brand guidelines and our set practices?
10. Do systems alarm me if the bias is not checked and starters getting into production?

Making AI Safe and Unique for Engineering Teams - 

The design of guardrails should focus on simplicity. The limits of LLM should be understood and clearly explained, the solution should be rules-driven armed with deep learning to comply with any custom compliance.

The solution must cater to and naturally blend with “Existing” practices of the business, rather than forcing a new set of working practices.

For instance, For an Engineering Team - The set of “development rules and process” is different than others, though at the outset both look the same - going deeper with a unique identity of such process and practices - and embedding them in Rules as Guardrails makes a solution acceptable for Engineering teams.

The engineering team must consider below before opting or building for generative AI solutions

1. The output matches your enterprise data, pattern, and development practices.LLM can’t hallucinate or exaggerate. 
2. Your data is handled with utmost rigor and security 
3. LLMs need to ensure that data, inputs, and outputs are accurate and referenceable. 

In conclusion, setting these Gaurarails is not optional or seen as a feature set, but built in the foundation models, right from development to deployment.  With an ability to make changes to these rules, and this may differentiate a unique full-stack LLM solution from a plug-in.