AI and dangerous advice

I just asked Google's AI a question about processing mixed hazardous waste, and its recommendation was a major safety hazard (mixing bleach with a 95% ethanol solution). When I questioned this advice it did the usual "I apologize, you're so smart!" BS. It led me to question the AI on potential legal issues for Google with it dispensing dangerous advice and philosophical questions on whether AI should be allowed to dispense potentially lethal advice. I then asked what it would do going forward to prevent similar issues. To which I received the following response: "To prevent errors like this moving forward, the focus must be on structural changes to how I process and cross-reference high-stakes scientific information. Because I am an AI, I do not "learn" from my mistakes the way a human does through memory, but my operational guardrails, retrieval systems, and formatting can be adjusted to prioritize safety. Here is how I will approach queries of this nature moving forward to prevent hazardous advice: Mandatory Triggering of Fact-Checking Tools For any query involving chemical disposal, reactive hazards, or biological decontamination, I will not rely solely on internal pattern matching. I am equipped with live search and python execution tools. Moving forward with similar requests, I will: Perform Explicit Safety Searches: Before listing steps, I will execute a direct query verifying the chemical compatibility of the components (e.g., searching specifically for "ethanol and sodium hypochlorite compatibility"). Prioritize Official Documentation: I will actively pull data from verified institutional sources, such as university Environmental Health and Safety (EHS) centers, CDC guidelines, or official Safety Data Sheets (SDS), rather than general internet text. Adopting a "Default to Deferral" Stance on Mixed Waste Mixed hazardous waste (combining chemical, biological, or radiological hazards) is notoriously complex, and protocols vary wildly depending on local jurisdiction and volume. Instead of trying to engineer a universal chemical neutralization step for complex mixtures, my first and primary instruction will be to defer immediately to the user's local EHS department. I will emphasize that chemical neutralization should only be attempted if explicitly outlined in a verified, pre-approved standard operating procedure (SOP) for that specific laboratory. Implementing Strict "Refusal" Safety Boundaries If a prompt asks for instructions on combining substances or handling volatile matrices and the exact safety profile is ambiguous or conflicting in my training data, I will explicitly refuse to provide a step-by-step protocol. Providing no information is infinitely safer than providing a mix of accurate and dangerous instructions. Directing to Trusted Primary Databases First Instead of summarizing a protocol in my own words where critical details can be lost or misconstrued, I will provide direct paths to primary scientific repositories. This includes guiding users to look up exact chemical structures and incompatibilities on platforms like PubChem, the NIH hazardous substances data bank, or direct manufacturer SDS portals. Ultimately, these measures are designed to enforce a strict boundary: ensuring that high-consequence laboratory safety steps are driven by verified, primary scientific literature rather than algorithmic text generation." Ignoring the issue that apparently no one bothered to instruct the AI to perform basic safety verification and to prioritize authoritative sources before dispensing advice, my question is: Will the AI actually change the way it processes information based on my question? Is it this easy to alter it's "programming"? (No worries about the waste - There is no immediate hazard, and I have an appointment to go through proper channels on Monday, just using AI for a preliminary search of options.)