The first time I trusted an LLM with a real decision, it gave me a crisp answer and a calm certainty that felt like competence. I shipped the deck. The number it cited, clean, plausible, was invented. In the silence after that realization, I heard what I now listen for in every decision: the faint pitch in the blackness.
LLMs are ungrounded language engines, not agents. If you anchor them to verified sources and treat their output as drafts, you get value without false certainty.
The LLM grounding problem is the gap between text patterns and real‑world meaning. Humans tie words to sights, sounds, and actions; LLMs tie words only to other words. Without sensory grounding, they can produce fluent but unfounded claims.
The practical line
You ask for a market stat; the answer arrives neatly phrased. It sounds right, fits your narrative, and buys you time, until it doesn't. Ungrounded fluency creates a special risk: plausible specifics.
LLMs map patterns in text. They don't see, touch, or measure. Without external facts at answer time, they fill gaps with the most likely phrase, not the true one. That's why retrieval‑augmented generation (RAG) helps: first pull evidence from a trusted source, then ask the LLM to write only from that evidence.
When the pitch is faint, you don't amplify it with confidence; you trace it with constraints.
Signal discipline in practice
You don't need more data; you need better discipline. You don't need a bigger LLM; you need better checks.
Anchor first, write second. Pull documents from a trusted base, docs, filings, your CRM. Then ask the LLM to answer only with citations to those files. This is classic RAG and it lowers risk because the answer is fenced by evidence.
Use a two‑step failsafe: retrieval from sources you name, then generation that refuses to use anything else. If a fact isn't in the sources, the answer should say “not in provided data.” For any number that could move money or reputation, require at least three independent citations.
We crave certainty because it relieves pressure. But certainty borrowed from fluency is counterfeit. Better to live with measured doubt, trade it for traceable evidence, and move when reality, not the narrative, permits.
Test before you trust
You won't talk your way out of this; you have to run small, reversible experiments.
Start with a draft‑then‑verify loop. Ask the LLM to write a short answer with inline citations, then spot‑check one citation you didn't expect. If it fails, stop and fix the retrieval step. Before scaling, pick one tough query and see if the LLM can cite the exact paragraph and page from your source. If it can't, your data pipeline, not your prompt, is the issue.
For any claim that changes a decision, budget a quick human verification pass. Ten minutes of checking beats ten hours of cleanup.
Decision hygiene under uncertainty
Short, then steady. You won't eliminate uncertainty, but you can bound it.
Write the decision question in one sentence. Make it specific enough that an answer could be wrong. Name acceptable sources up front, if the LLM can't cite them, you don't have an answer. Pre‑commit how you'll act if evidence is partial. This is decision hygiene: define “good enough to move” before the heat of the moment.
Scale reduces some errors but can't conjure sensory grounding. Without source‑level constraints, fluent guesses remain.
Field evidence
A CEO asked me to automate light due diligence for vendor selection. First pass looked great, until the LLM hallucinated a revenue figure. We added retrieval from filings and contract PDFs, forced inline citations, and made “not in data” an acceptable answer. False specifics dropped; the team earned time back without bluffing.
A marketing manager used RAG to draft competitor notes from a private repository of case studies. They kept a one‑line log of every claim that changed a decision and who verified it. The habit exposed one bad source and improved the repository instead of masking the problem with longer prompts.
The shift that matters
When you hear that thin whine of certainty without proof, treat it as a warning, not a green light. The shift is simple and hard: move from fluency‑seeking to evidence‑seeking. That's where small, traceable moves compound and your thinking stack gets quieter, cleaner, truer.
If you want ongoing help with this shift, I send a weekly brief with one clear idea and one small experiment to apply it. You'll get a plain‑English concept that separates AI hype from operational reality, a field‑tested exercise you can run in under an hour, and proof of utility, we translate complex ideas into practical moves and show the line from idea to action.
Reply or sign up, and make your next LLM decision the most grounded one you've made this quarter.
What's the one claim you'll demand evidence for today?
Here's something you can tackle right now:
Before using any LLM output for decisions, ask: ‘What specific sources support this claim?' If you can't trace it to evidence, treat it as a draft, not fact.
