Earlier quoted context omitted.
This problem is inherently unsolvable because LLMS are prone to hallucinations and prompt injection attacks. I think that you're insinuating that these things can be fixed, but to my knowledge, both of these problems are practically unsolvable. If that turns out to be false, then when they are solved, fully autonomous AI agents may become feasible. However, because these problems are unsolvable right now, anyone who…
>> This problem is inherently unsolvable because LLMS are prone to hallucinations and prompt injection attacks. Okay, but aren't you making the mistake of assuming that we will always be stuck with LLMs, and a more advanced form of AI won't be invented that can do what LLMs can do, but is also resistant or immune to these problems? Or perhaps another "layer" (pre-processing/post-processing) that runs alongside LLMs?
Re: OpenClaw is a security nightmare dressed up as a daydream
#321We built a correction layer that does this — the model verifies its output against your prompt during generation, not after. Same API call, no retries.
Budget models without it: 40-50% accuracy. With it: 95.7% on 10k+ clinical documents. Hallucinations aren't eliminated — some might still fail — but every failure is explicitly flagged. No silent errors. and it improves over time to give you better results next time.
It doesn't make hallucinations "solved. 100%". It makes them an engineering problem with a measurable - very low error rate you can drive down over time.
We're calling it LiveFix — livefix.ai. Benchmarked across all frontier and budget models.