Systems architect & technical product leader with roots in bare-metal engineering. I design modern local-first, data-sovereign AI platforms in Go/Python and scale elite core infrastructure teams.
This is a great primer on the 'why' behind hallucinations. Most people assume AI is a database when it’s actually a reasoning engine—and those two things have very different relationship statuses with the 'Truth.'
However, from an Infrastructure Thinking perspective, the goal isn't just to understand why it lies, but to build a system where those lies can't reach the end-user. I’ve been working on a pattern I call the Sovereign Gateway, which treats the LLM as an untrusted agent. Instead of just hoping the model doesn't hallucinate, we use Versioned Snapshots and Forensic Integrity Checks to validate the output against a 'Ground Truth' database—like the SQL transactions and procedures mentioned in other foundational stacks—before the data is ever surfaced.
In my Sovereign Synapse series, I argue that the 'Staleness vs. Latency' trade-off is often where these hallucinations hide. If the data pipeline is too slow, the agent 'fills in the gaps.' By moving toward Shadow-Routing logic, we can audit the agent's forensic integrity in real time.
The 'Why' is important, but for those of us building production-grade AI, the 'How do we contain it' is the real challenge.
Thank you for the details Ken. The how is definitely a bigger challenge for production grade systems. The evaluations and ground truth are now so much more important! I would love to read more on your patternm, can you please point me to the right links?
Systems architect & technical product leader with roots in bare-metal engineering. I design modern local-first, data-sovereign AI platforms in Go/Python and scale elite core infrastructure teams.
@rohini_gaonkar It's still a work in progress, but you might start with my Who Audits the Auditors? post and follow that series. My deeper dive series should be coming out starting next week. I'm excited to get your feedback along the way.
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This is a great primer on the 'why' behind hallucinations. Most people assume AI is a database when it’s actually a reasoning engine—and those two things have very different relationship statuses with the 'Truth.'
However, from an Infrastructure Thinking perspective, the goal isn't just to understand why it lies, but to build a system where those lies can't reach the end-user. I’ve been working on a pattern I call the Sovereign Gateway, which treats the LLM as an untrusted agent. Instead of just hoping the model doesn't hallucinate, we use Versioned Snapshots and Forensic Integrity Checks to validate the output against a 'Ground Truth' database—like the SQL transactions and procedures mentioned in other foundational stacks—before the data is ever surfaced.
In my Sovereign Synapse series, I argue that the 'Staleness vs. Latency' trade-off is often where these hallucinations hide. If the data pipeline is too slow, the agent 'fills in the gaps.' By moving toward Shadow-Routing logic, we can audit the agent's forensic integrity in real time.
The 'Why' is important, but for those of us building production-grade AI, the 'How do we contain it' is the real challenge.
Thank you for the details Ken. The how is definitely a bigger challenge for production grade systems. The evaluations and ground truth are now so much more important! I would love to read more on your patternm, can you please point me to the right links?
@rohini_gaonkar It's still a work in progress, but you might start with my Who Audits the Auditors? post and follow that series. My deeper dive series should be coming out starting next week. I'm excited to get your feedback along the way.