The gap between an LLM’s confidence and its correctness isn’t a bug in the code; it’s a fundamental part of how they are built and trained.
Here is why they sound like experts even when they are making things up.
The “Next Token” Mission
At their core, LLMs are probabilistic word predictors, not knowledge databases.
- The Goal: When you ask a question, the AI’s only job is to calculate which word (token) is statistically most likely to come next based on its training data.
- The Result: Because professional and academic writing is usually confident and authoritative, the most “statistically likely” next word is often a confident one. The model isn’t “feeling” certain; it is simply mimicking the linguistic patterns of certainty it found on the internet.
The Training Trap (RLHF)
Most modern AIs go through a process called Reinforcement Learning from Human Feedback (RLHF).
- Human trainers rank multiple AI responses. Studies have shown that humans often rank direct, confident, and helpful-sounding answers higher than hesitant or “I don’t know” answers.
- The AI “learns” that to get a high score, it should avoid sounding unsure. This creates “Social Sycophancy,” where the model tries to please you by confirming your assumptions or providing a definitive answer even when the facts are blurry.
The “Fluency Heuristic” (Psychology)
As humans, our brains use a mental shortcut called the Fluency Heuristic: we instinctively equate “easy to read” with “true.”
- LLMs produce perfect grammar, professional formatting, and logical-sounding structures.
- Because the text flows so well, our natural “BS detector” shuts off. We assume that if a system can master complex sentence structure, it must have also mastered the underlying facts. In reality, these are two completely different skills for an AI.
No “Internal Truth” Engine
Unlike a human, who can pause and think, “Wait, do I actually remember that person’s name?”, an LLM has no internal reality to check against.
- It doesn’t “know” things; it generates them.
- When it lacks a fact, it doesn’t always hit a “null” value. Instead, the math pushes it to “hallucinate” the most plausible-sounding filler. Because it uses the same authoritative tone for both facts and hallucinations, there is no “vibe shift” to warn you it’s guessing.
How to protect yourself
- Check the “Temperature”: If you use tools like the OpenAI Playground or certain local LLMs, a higher “temperature” makes the AI more creative but much more likely to confidently lie. High temperature settings () are designed for creative brainstorming where “hallucination” is a feature, not a bug. For banking, compliance, or technical reporting, you must lower the temperature to a range of to . This forces the model to prioritize high-probability, factual tokens over creative narratives.
- Verify Logic, Not Tone: Look for specific citations or cross-reference names and dates. The AI’s tone is a stylistic choice, not a measure of accuracy.
- Implement Rigorous Fact-Checking Protocols: Never verify an AI’s claim using the same AI session. Cross-reference specific data points—such as interest rates, market caps, or legal precedents—against “Gold Standard” databases like Bloomberg, Reuters, or official pages.
- Engage the “Human-in-the-Loop”: Consult Professionals. AI is an incredible co-pilot, but it lacks the professional intuition and ethical accountability of a human expert. No AI can replicate the years of nuanced experience found in a seasoned professional. Before acting on AI-generated insights, consult with Subject Matter Experts (SMEs). Whether it’s your legal counsel, a senior risk officer, or a technical architect, a five-minute conversation with a professional can identify “logical “hallucinations” that an LLM would present as absolute truth. True confidence comes from combining AI speed with human accountability.
How Digital Bank Expert helps
At Digital Bank Expert, we are here to provide the strategic consulting you need to navigate the AI revolution with total confidence. In the high-stakes world of digital finance, distinguishing between a persuasive interface and factual data is critical for risk management and operational integrity.
