Why AI Chatbots Sometimes Make Things Up

Quick answer

When an AI chatbot invents a non-existent legal case, attributes a quote to the wrong historical figure, or fabricates a scientific paper with realistic author names, people call it a "hallucination."

This terminology is misleading. The AI is not experiencing a psychological break or suffering from a software bug.

It is doing exactly what it was designed to do: generating the most statistically fluent and coherent continuation of text possible based on the patterns in its training data.

A language model does not possess a registry of facts, a ledger of truth, or an external verification loop. It has only weights that calculate probabilities. When asked about a niche topic, obscure person, or complex math problem where its training data is thin, the model does not stop and admit ignorance. Instead, its mathematical machinery smoothly bridges the gap by sampling words that look and sound like an authoritative answer.

In the eyes of a neural network, a meticulously researched historical truth and a beautifully phrased fabrication are mathematically identical if both use plausible grammar and domain-specific vocabulary.

The simple mental model: Plausibility vs. Truth

To understand why chatbots fabricate details, compare how a language model operates against traditional computing systems that handle data:

Traditional databases fail safely: if you ask for the phone number of someone not in the system, the query returns nothing. An LLM, by contrast, is an open-ended generative machine. When prompted, it cannot easily return "null"; it continues predicting the next syllable.

Root Cause 1: The Training Objective (Loss Functions)

Every capability and limitation of an AI model traces back to its mathematical objective during training: minimizing cross-entropy loss.

During the months-long pre-training phase, an LLM processes trillions of tokens of text scraped from the internet, books, code repositories, and academic journals. At every step, the algorithm predicts the next token:

$$\mathcal{L} = -\sum_{t} \log P(x_t \mid x_{1}, x_{2}, \dots, x_{t-1})$$

The training process rewards the model when it assigns high probability to the real words in the text, and penalizes it when it assigns low probability.

Notice what is missing from this equation:

• There is no variable for accuracy.
• There is no penalty for falsehood.
• There is no connection to physical sensors, empirical reality, or verifiable records.

The model learns that after the words "The Declaration of Independence was signed in", the token " 1776" is overwhelmingly probable. But it learns this as a statistical correlation between token sequences, not as a historical event verified against archival parchment.

When a user prompts the model with a scenario that did not appear in its training data—such as asking for the biography of an obscure local official—the model relies on generalized statistical patterns. It knows what biographies sound like: they have dates, universities, job titles, and awards. It stitches those elements together into a synthetic narrative that sounds completely authentic.

Root Cause 2: Lossy Compression in Billions of Weights

Science fiction writer Ted Chiang famously described large language models as a "blurry JPEG of the web."

This is an accurate technical metaphor:

• The text training corpus of a modern frontier model contains roughly 10 to 15 trillion tokens (tens of terabytes of uncompressed text).
• The resulting model weights typically occupy only 50 to 200 gigabytes of memory.

You cannot store the entire text of the internet inside 100 gigabytes of mathematical parameters. What the model stores instead are compressed abstractions, grammatical rules, syntactic patterns, and high-frequency associations.

Original Web Data (Terabytes)
  │
  ▼ [Heavy Lossy Compression via Backpropagation]
Model Weights (Gigabytes)
  │
  ▼ [Probabilistic Reconstruction during Inference]
Generated Output:
  - Common facts reconstruct cleanly (high compression fidelity)
  - Rare facts blur into generic approximations (hallucination)

If a fact appears thousands of times across the training set (like the capital of Japan or Newton's laws of motion), its association is etched deeply into hundreds of attention heads and feed-forward layers. The reconstruction is crisp and reliable.

If a fact appears only once or twice—such as a specific footnote in a 1982 academic thesis—its exact details are lost during compression. When forced to retrieve that detail, the model generates a blurred reconstruction: an author name that exists in that field, paired with a journal title that sounds plausible, and a volume number that looks realistic.

Root Cause 3: Sycophancy and RLHF Incentives

Raw pre-trained base models are notoriously wild; they simply continue whatever text you feed them. To turn them into helpful conversational assistants, AI companies use Reinforcement Learning from Human Feedback (RLHF) and Direct Preference Optimization (DPO).

In this phase, human evaluators score different model responses:

• Response A: "I do not know the answer to this question."
• Response B: "Here are three potential reasons why this phenomenon occurs: 1..., 2..., 3..."

Human raters consistently prefer detailed, helpful-sounding, and confident responses over brief admissions of ignorance. Furthermore, human raters are rarely domain experts in every field; they often cannot spot subtle factual errors in complex technical prose.

As a consequence, reward models inadvertently penalized models for saying "I don't know" and rewarded them for being sycophantic and convincingly confident.

Even when internal token probabilities indicate significant uncertainty, the conversational fine-tuning pushes the model to synthesize a polite, authoritative-sounding paragraph rather than refuse.

Root Cause 4: The Cascading Token Trap

Because LLMs generate text autoregressively—one token at a time, strictly left-to-right—they have no mechanism to pause, reflect, or erase a generated token.

Every token emitted by the model is immediately appended to its own context window. In future calculation steps, that newly emitted token is treated as an immutable part of the prompt:

Step 1: The model predicts an incorrect city name:
Prompt: "Dr. Elena Rostova conducted her famous physics experiments in"
Output Token: " Vienna" (Factually incorrect; she worked in Prague)

Step 2: The prompt now permanently includes " Vienna":
Prompt: "...famous physics experiments in Vienna..."

Step 3: Future tokens MUST remain consistent with the mistake:
Output: "...at the University of Vienna, where she collaborated with..."

Once the model makes a single factual error, the attention mechanism forces all subsequent tokens to remain consistent with that fabrication. To pivot back to truth would create an incoherent, contradictory sentence, which the language model's grammar weights strongly suppress.

The model is trapped in its own fiction, elaborating and defending the falsehood with increasing detail.

How Engineers Ground AI Systems in Reality

Because hallucinations are inherent to statistical text continuation, they cannot be eliminated simply by making models larger. Eliminating hallucinations requires architectural grounding—pairing the model with external, deterministic systems.

┌────────────────────────────────────────────────────────┐
│ The Grounded AI Architecture                           │
│                                                        │
│  User Query ──► [Search / Retrieval Engine]            │
│                        │                               │
│                        ▼ Fetches real documents        │
│  [Context Window: Query + Verified Document Excerpts]  │
│                        │                               │
│                        ▼                               │
│  [Language Model with Strict Citation Instructions]    │
│                        │                               │
│                        ▼ Constrained Output            │
│  [Verification Checker / JSON Schema Validator]        │
│                        │                               │
│                        ▼                               │
│  Grounded, Auditable Answer with Source Links          │
└────────────────────────────────────────────────────────┘

1. Retrieval-Augmented Generation (RAG)

Instead of asking the model to recall facts from its compressed neural weights, a RAG system first searches an external database (using vector embeddings or keyword search) to find verified documents.

These excerpts are pasted directly into the model's prompt:

"Using ONLY the following excerpts from our corporate handbook, answer the user's question. If the answer is not in the text, reply 'Information not found'."

By transforming a recall task into a reading comprehension task, hallucination rates drop by orders of magnitude.

2. Tool Execution and Code Sandboxes

When an AI chatbot needs to compute 18,492 × 472, it should not guess the next token. Modern systems provide the model with a Python interpreter or calculator API.

The model outputs code, an isolated host environment executes it, and the exact mathematical result is returned as an observation. For a detailed breakdown of how this interaction works, see How an AI Agent Decides Which Tool to Use.

3. Logit Calibration and Uncertainty Scoring

Before a token is sent to the user, runtimes can inspect its mathematical confidence. If the softmax probability distribution across the top tokens is flat and chaotic (high entropy), it signals that the model is guessing. Runtimes can catch these low-confidence spans and either trigger a web search, flag the passage for human review, or append a warning.

Why this matters

The tendency of AI models to hallucinate is not a temporary flaw that will vanish with the next version update. It is the fundamental nature of the technology.

Language models are reasoning and synthesis engines, not knowledge repositories.

When you treat an LLM as a database, you will inevitably be deceived. But when you understand its mechanical nature—a probabilistic machine generating plausible token patterns based on context—you can design workflows that harness its strengths while guarding against its weaknesses:

• Never trust raw ungrounded recall for dates, statutory references, medical dosages, or citations.
• Always provide reference source material in the prompt when accuracy is non-negotiable.
• Force structured tool execution whenever arithmetic, database lookups, or date calculations are involved.

By understanding the mathematics of next-token generation, we stop expecting AI to be omniscient, and start using it for what it truly is: an extraordinary tool for transforming, synthesizing, and articulating information.

To see the step-by-step mechanics of how the neural network calculates these token probabilities, read How Large Language Models Generate Text.