Why AI Models Hallucinate and How to Prevent It.

AI hallucinations are one of the most fascinating challenges we're tackling in this space. The good news?

Understanding why they happen is the first step to working around them effectively.

Why AI Models Hallucinate

AI hallucinations occur when models generate plausible-sounding but incorrect, misleading, or fabricated information. Unlike human hallucinations caused by brain disorders, AI hallucinations stem from the fundamental way these systems work—they're pattern-prediction machines, not knowledge databases.

The Core Problem: Guessing Over Uncertainty

OpenAI's latest research reveals that language models hallucinate because their training and evaluation procedures reward guessing over acknowledging uncertainty. When an AI doesn't know something, it doesn't say "I don't know"—instead, it predicts the most statistically probable next word based on patterns it learned, even when that leads to fabrication.

Root Causes Include:

Insufficient or flawed training data: If the training dataset is incomplete, biased, outdated, or contains inaccuracies, the model learns incorrect patterns. The quality of outputs is directly tied to the quality of inputs used during training.

Lack of real-world grounding: Most models generate outputs based on training data patterns rather than accessing verified knowledge bases, meaning they can't distinguish between what's plausible and what's true.

Probabilistic nature: AI predicts sequences based on statistical likelihood, not factual accuracy. It generates what sounds right, not necessarily what is right.

Overfitting: When models memorize specific training examples rather than learning generalizable patterns, they apply irrelevant or inappropriate information to new contexts.

Model complexity: Highly complex models without proper constraints can produce a wider range of outputs, increasing the likelihood of hallucinations.

Encoding and decoding errors: Mistakes in how the model processes information—learning wrong correlations or attending to the wrong parts of input—can produce hallucinated outputs.

Scenarios and Behaviors That Trigger Hallucinations

Understanding when hallucinations are most likely helps you avoid them proactively.

High-Risk Scenarios:

Vague or ambiguous prompts: Unclear instructions force the model to make assumptions, leading to generic or incorrect responses. Asking "Tell me about space" versus "Summarize NASA's recent Mars missions with factual details from official reports" produces vastly different reliability levels.

Complex or multi-step queries: Cramming multiple topics into one prompt increases confusion and inaccurate outputs. Breaking requests into focused, sequential prompts dramatically improves accuracy.

Domain-specific or specialized knowledge: Hallucination rates vary by domain. Medical information averages 4.3-15.6% hallucination rates, legal information 6.4-18.7%, and scientific research 3.7-16.9%.

Requests for citations or sources: When asked for references, AI may fabricate plausible-sounding but completely nonexistent citations, articles, or legal cases. One study found ChatGPT falsely attributed 76% of quotes from journalism sites.

Adversarial or nonsensical prompts: Deliberately confusing inputs or prompts composed of random tokens can trigger hallucinations. This reveals that hallucinations share characteristics with adversarial examples in machine learning.

Idioms and slang: Expressions the model wasn't trained on can lead to nonsensical outputs.

Out-of-date information: When models lack current data, they rely on outdated training information, producing factually incorrect responses.

Using external tools: LLM agents augmented with external tools can exhibit significantly higher hallucination rates due to added complexity.

Temperature Settings:

The temperature parameter controls randomness in outputs. Higher temperatures (0.7-1.0) increase creativity but also hallucination risk, while lower temperatures (0-0.3) produce more focused, factual responses. However, setting temperature to zero doesn't eliminate hallucinations—it only makes outputs deterministic.

Cascade Effect:

In conversational AI, each generated word influences the next. If the model starts with a hallucination, it can build upon that error, creating increasingly inaccurate responses as the conversation continues.

Best Practice Instructions to Reduce Hallucinations

Here's your practical playbook for minimizing AI hallucinations in business applications:

1. Use Retrieval-Augmented Generation (RAG)

RAG is the gold standard for reducing hallucinations. It connects AI models to external, verified knowledge bases—like your company's documentation, trusted databases, or authoritative sources—before generating responses. Instead of relying solely on training data, the model retrieves relevant information from trusted sources first, then generates answers grounded in that data.

Benefits: Reduces hallucinations, eliminates expensive retraining, provides source citations users can verify, and keeps information current.

2. Engineer Clear, Specific Prompts

Precision in prompting is non-negotiable:

• Be specific: Replace "Tell me about marketing" with "What are three specific strategies to increase customer retention for B2B SaaS companies in 2025?" 

• Provide context: Include relevant background information, audience details, and desired format 

• Break down complex requests: Split multi-part questions into sequential, focused prompts 

• Set clear expectations: Use specific language that guides the model toward known data sources 

• Request verification: Ask the AI to cite sources or explain its reasoning step-by-step 

• Specify constraints: Define word count, format, time frame, or other boundaries to focus outputs

3. Use Chain-of-Thought (CoT) Prompting

Ask models to break down their reasoning step-by-step before arriving at answers. This technique improves accuracy by 35% in reasoning tasks and reduces mathematical errors by 28%. Example: "Break down the steps to calculate 17 multiplied by 24 before giving the final answer."

4. Implement Chain-of-Verification (CoVe)

This four-step process reduces hallucinations:

• Generate a baseline response

• Create verification questions to test accuracy

• Execute verification by checking responses against original answers

• Generate final response with corrections applied

5. Adjust Temperature Settings Appropriately

Use low temperature settings (0-0.3) for factual tasks requiring accuracy. Use higher temperatures (0.7-1.0) only for creative, open-ended tasks like brainstorming where factual precision is less critical.

6. Establish Human-in-the-Loop (HITL) Processes

76% of enterprises now include human review processes to catch hallucinations before deployment. Human oversight provides essential fact-checking that AI cannot perform on its own. Knowledge workers currently spend an average of 4.3 hours per week fact-checking AI outputs.

7. Fine-Tune on High-Quality Data

When you have standardized tasks and sufficient training data, fine-tuning models on curated, accurate, domain-specific information significantly reduces hallucinations. Ensure data comes from verified, reputable sources with biases removed.

8. Implement Post-Processing Verification

Compare AI-generated outputs against reliable factual databases. Cross-reference information with trusted sources like academic databases, official government sites, or peer-reviewed publications. Use fact-checking tools and always verify citations actually exist.

9. Direct AI to Acknowledge Uncertainty

Instruct models that "no answer is better than an incorrect answer". This reduces the likelihood of fabricated responses when the AI lacks sufficient information.

10. Use Constrained Prompting

Limit the model's output by providing strict guidelines on format, length, tone, and acceptable sources. Example: "Using only information from Wikipedia, provide three bullet points explaining..."

11. Provide Examples in Prompts (Few-Shot Learning)

Including 2-3 correct examples in your prompt guides the AI toward the type of information you're requesting.

12. Use Trusted, Domain-Specific Models

Generic models like ChatGPT work for general tasks, but domain-specific models trained on specialized datasets produce fewer hallucinations in their areas of expertise. Consider using models specifically designed for your industry or use case.

13. Maintain Up-to-Date Knowledge Bases

If using RAG, ensure your external data sources are regularly updated through automated real-time processes or periodic batch processing.

14. Avoid Information Overload

Don't cram excessive information into single prompts. This causes confusion and incoherent responses. Focus each prompt on a single, manageable task.

15. Establish Verification SOPs

Create standard operating procedures for consistent review, including what to check, how to correct errors, and how to flag edge cases. Train teams on interpreting AI results and when to intervene.

The Reality Check

Even with best practices, hallucinations cannot be completely eliminated with current AI technologies. Leading models like Google Gemini-2.0-Flash-001 achieve hallucination rates as low as 0.7-0.9%, while most widely-used models fall between 2-5%. Paradoxically, more advanced reasoning models sometimes hallucinate more frequently—OpenAI's o3 and o4-mini hallucinated 33% and 48% respectively on certain benchmarks.

The key is implementing layered safeguards: RAG for grounding, precise prompting for clarity, verification processes for accuracy, and human oversight for accountability. This combination creates trustworthy AI systems suitable for business applications.

The opportunity here is massive. Organisations that master hallucination mitigation will build more reliable AI systems, earn greater user trust, and unlock applications in sensitive domains like healthcare, finance, and legal services. Understanding these challenges positions your consultancy to guide New Zealand businesses toward AI implementations that actually deliver on their promises.