When a software engineer writes code, they write tests. When an LLM generates output, what's the equivalent?
This question sits at the center of a genuine unsolved problem. The answer will shape where AI can be safely deployed—and where it can't.
Understanding the Mechanism
Large language models work by predicting the most likely next token based on context and training data. This is genuinely useful—it's how they generate coherent, contextually appropriate text across a remarkable range of tasks.
It's also why hallucinations occur.
When training data is incomplete or context is ambiguous, the model still produces something. It has to—that's what it's designed to do. The architecture doesn't distinguish between "I know this" and "this seems plausible." Both produce confident-sounding output.
This is a characteristic of how transformers work and how many LLMs are trained. The same mechanism that enables fluid generation also enables confident fabrication.
Recent work on attention mechanisms helps with one constraint. DeepSeek's Sparse Attention reduces the computational cost of processing long contexts from O(L²) to approximately O(kL), making it practical to give models more information to work with. But more context doesn't eliminate the fundamental issue—it just changes where hallucinations are likely to occur.
The Scaling Challenge
Human review works at low volume. An expert can verify ten LLM outputs per hour with reasonable accuracy. Maybe twenty if they're familiar with the domain.
At production scale, the math stops working.
A single LLM can generate thousands of outputs per hour. Even a small deployment creates a verification backlog that grows faster than humans can clear it. Add cognitive fatigue—accuracy drops after the fiftieth review—and the gap widens.
This isn't a criticism of AI. It's a statement about operational reality. Generation is fast. Verification is slow. The asymmetry is structural.
The software industry faced this same problem decades ago. Their solution wasn't more reviewers. It was automation.
What Software Engineering Teaches Us
Software verification rests on a simple insight: define expected behavior, then check automatically.
A test is a small program that runs another program and verifies the output matches expectations. Run thousands of tests in seconds. Repeat on every change. Catch regressions before they reach production.
Type systems go further—they prevent entire categories of errors at compile time. You can't pass a string where a number is expected. The constraint is structural, not behavioral.
Formal verification goes further still—mathematical proofs that code satisfies specified properties. Rarely used in practice due to cost, but available when stakes justify it.
The key pattern: verification is a system property, not a human activity. Engineers design verification into their tools and workflows. The machine does the checking. Humans define what "correct" means.
This doesn't mean software is perfect. Bugs slip through. Tests can be wrong. But errors are discoverable and reproducible. When something breaks, you can find out why.
What's the equivalent for LLM output?
Emerging Approaches
The research community is actively working on this problem. Several directions show promise, though none are fully mature.
Neuro-symbolic hybrids combine LLM flexibility with formal constraints. ToolGate, a recent framework from Zhejiang University, wraps tool calls in Hoare-style contracts—preconditions that must be satisfied before execution, postconditions verified after. The LLM generates; the symbolic system gates. Claims only propagate through verified channels.
Evidence-bound execution takes this further. Systems like EviBound require machine-checkable evidence for every claim. No artifact, no acceptance. In controlled experiments, this approach eliminated hallucinated claims entirely—though scaling to open-ended generation remains an open question.
Structured uncertainty addresses the problem from another angle. Rather than detecting hallucinations after the fact, force models to surface uncertainty explicitly. Recent work on uncertainty as a control signal shows that a well-designed output schema can include "I cannot determine this" as a valid response. The model isn't asked to guess when confidence is low.
Corpus grounding shifts verification away from model confidence (which is unreliable) toward external facts. QuCo-RAG checks entity co-occurrence in training data—if two concepts never appeared together in training, claims about their relationship warrant skepticism.
These are research directions, not production-ready solutions. Intellectual honesty requires acknowledging the gap between promising papers and deployed systems.
Every successful verification approach shares a common architecture: the LLM generates, but a deterministic system gates what gets accepted. The creative capability stays; the unchecked confidence goes. This is the same pattern that makes software reliable—separation between creation and verification.
What This Means for Institutional Knowledge
The verification gap has a compounding effect on business operations.
When AI outputs can't be verified, organizations face a choice: either slow down to human-review everything, or accept the risk of propagating errors. Neither option scales.
The organizations solving this aren't just adding more review layers. They're building what we call a knowledge layer—infrastructure that captures institutional context, tracks decisions, and provides ground truth for AI to verify against.
When an AI makes a claim about your business, it should be checkable against what your organization actually knows. Not against the model's training data. Against your documented decisions, your defined terms, your historical patterns.
This is the difference between AI that sounds right and AI that is right in your context.
Practical Implications
Where does this leave organizations deploying LLMs today?
The honest answer: domain selection matters enormously.
LLMs excel in contexts where errors are low-cost and easily corrected. Drafting emails. Summarizing documents. Generating first drafts for human refinement. The human remains in the loop, but their role shifts from creation to curation.
High-stakes decisions still require human judgment. Medical diagnoses. Legal analysis. Financial recommendations. Not because AI can't help—it often can—but because verification mechanisms aren't yet mature enough to catch the errors that matter.
The verification gap is real. Organizations that acknowledge it can work around it. Organizations that pretend otherwise take on risk they may not fully understand.
Questions worth asking when evaluating AI systems:
- How does the system handle uncertainty? Does it surface "I don't know" or guess confidently?
- What verification mechanisms exist between generation and action? Is there a gate, or does output flow directly to use?
- How are errors detected and corrected? Is there a feedback loop, or does drift accumulate silently?
- What's the cost of an undetected error in this context? This determines how much verification infrastructure is worth building.
The Path Forward
The path forward isn't hype or fear. It's engineering.
The same discipline that gave us reliable software—testing, type systems, formal methods—can give us reliable AI systems. The mechanisms will differ, but the principle holds: define correctness, then verify automatically.
We're not there yet. The research is active, the problems are hard, and honest practitioners will acknowledge uncertainty about timelines.
But the work is underway. And organizations that understand the trust gap—that take verification seriously rather than assuming it away—will be better positioned when mature solutions arrive.
The verification layer isn't optional. It's what turns impressive demos into production systems you can actually trust.
