Garbage In, Garbage Out Is Simple to Understand but Difficult to Execute

The phrase — Garbage in, Garbage out — pops up naturally in most AI conversations. Everyone nods. It's self-explanatory. But when it comes to talking about the fix, most draw a blank.

Before we jump into GIGO — it's important to understand why the phrase was coined, what it meant, and why it swung back to relevance.

Origin Story of Garbage In, Garbage Out

In 1957, a US Army specialist named William Mellin was explaining early computers to a reporter. These machines cannot think, he said. Feed them a bad calculation, they'll process it faithfully and return something useless. Programmers started calling it GIGO — Garbage In, Garbage Out. By 1963 it was common jargon at IRS processing centers, where mispunched cards produced wrong refunds and literal bins of discarded magnetic tape. The problem was physical. The output was obviously broken.

Obviously — that problem got solved. Now, both code quality and data quality are well understood terms — mature disciplines. These systems deal with structured data inputs. Not knowledge.

This problem gets pushed downstream when agents execute based on poor knowledge. Agents that read every protocol, every policy, every guideline, and apply them across thousands of decisions. The problem is when enterprises jump straight from "we have documents" to "we have an AI agent" — skipping the step in between entirely.

How Do You Know If You Have a Garbage In Problem?

You start with garbage out first. A clinician pulls up a patient summary from an AI bot. It's coherent. Well structured. Sourced from the same clinical protocols the hospital spent months documenting. She trusts it. Why wouldn't she?

What if two of those protocols disagree on the dosing threshold? One was updated 3 months ago. The other wasn't. Both pushed to the same knowledge base. The model blends them into a single answer.

You Spotted Garbage Out. What Next?

The typical response: "this doesn't seem correct. Knowledge is incomplete, add more." The knowledge base grows. The problem compounds. More documents means more versions of the same facts. More versions means more surface area for contradiction.

Context windows don't filter for authority. The model blends across all of it, and the blend gets smoother and more confident the more material it has.

Why Audit Trails Are Critical for Correct Diagnosis

LLMs will always generate things outside your knowledge. That's what they do. You cannot engineer your way to zero hallucination.

What one can do — and this is the part I wish I'd understood earlier — is tell the two kinds of failure apart. Because without an audit trail, everything looks the same.

How Do You Fix Garbage In?

AI cannot resolve truth. It can surface conflict — flag that two documents disagree, identify which version is newer. But the decision about which version is actually true? That belongs to a human. A domain expert.

Truth in a regulated industry is a judgment call, and it always needs a name attached to it. You need audit trails for the knowledge itself — who resolved a conflict, when, what the previous version said, why it changed.

Same thing applies when external contracts and policies change. Someone inside your organization needs to explicitly map that change to your internal SOPs and sign off. Not implicitly adopt it. Not hope the AI notices.

Then there is the operational reality. Businesses run on Slack, WhatsApp, email chains. That knowledge is real. Your agents don't have it. Until there's a curation layer for conversational sources, your knowledge base will always be incomplete.

The Self-Diagnostic

I built a self-diagnostic around this. Seven questions — answering these takes less time than making instant coffee. Most organizations I've shown it to get stuck by question two.

Take the Knowledge Audit →