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Even with structured outputs and strict prompting, LLMs can return unexpected content. Implement output validation layers: JSON schema validation, regex checks on critical fields, confidence scoring, and human-in-the-loop reviews for high-stakes decisions. Never trust raw LLM output in production without a safety net.

Always set max_tokens in your API calls to prevent runaway generation. A missing token limit on a recursive agent loop can generate tens of thousands of tokens in seconds, leading to unexpected bills.

Never execute LLM-generated code in a privileged environment without sandboxing. Use containers, restricted runtimes, or code review gates to prevent generated code from accessing filesystems, networks, or databases it should not touch.

""The thing I lose the most sleep over is the overall safety of this technology.""

Never blindly trust the output of an LLM, especially for factual information or code. Always have a validation step, whether it's manual review, fact-checking against reliable sources, or running generated code in a sandboxed environment. LLMs can and do make mistakes.

For high-stakes applications (e.g., medical, legal, financial), do not fully automate decisions with an LLM. Implement a 'human-in-the-loop' workflow where the AI provides suggestions or analysis, but a qualified human makes the final decision. This is a critical safety practice.

Challenge: Create a system that can detect potential jailbreak attempts. First, research common jailbreak techniques (without implementing harmful ones). Then, design prompts that test the model's ability to recognize and respond appropriately to subtle manipulation attempts.

"The real risk with AI isn't malice but competence. A super-intelligent AI will be extremely good at accomplishing its goals, and if those goals aren't aligned with ours, we have a problem."

If your application involves user-generated content that is then processed by an LLM, or if the LLM generates content for users, implement content filtering for harmful or inappropriate material.

Never blindly trust code generated by an LLM. Always thoroughly test it for correctness, security vulnerabilities, and performance issues before using it in production.

Be mindful of how your AI application could be misused (dual-use problem). Design with safety and responsible AI principles in mind from the start.

Use LLMs to help moderate user-generated content by flagging potentially harmful, inappropriate, or off-topic posts for human review.

"The real risk with AI isn't malice but competence. A super-intelligent AI will be extremely good at accomplishing its goals, and if those goals aren't aligned with ours, we have a problem."

Challenge: Create a system that can detect potential jailbreak attempts. First, research common jailbreak techniques (without implementing harmful ones). Then, design prompts that test the model's ability to recognize and respond appropriately to subtle manipulation attempts.