LLM hallucinations: risks and how to mitigate them

As large language models (LLMs) like Llama, Gemini, ChatGPT, or Claude are integrated into products and business processes, an uncomfortable question becomes central: what do we do about LLM hallucinations?

It is relatively simple to build an impressive prototype over a weekend. But it is a very different matter to take generative AI to production without putting your users, your brand, or your regulatory compliance at risk. In this article, we explain, with a technical yet accessible approach:

• What LLM hallucinations.
• are. Why they occur from the model's perspective.
• Real-world cases and their consequences.
• How to interpret them: technical failure, bias, or poorly defined expectations?
• Concrete strategies to mitigate them in production using guardrails, human validation, and monitoring.

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Throughout the text, you will find references to other key content, such as our guide on LLM evaluation and major benchmarks and how to adapt your SEO strategy for LLM-based searches, where we dive deeper into the new search layer powered by generative models.

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What exactly are LLM hallucinations?

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In the context of AI, an LLM hallucination occurs when the model generates a response:

• Grammatically correct,
• Coherent in appearance,
• But lacking any basis in reality or verifiable data.

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In other words, the model "makes up" information: from an incorrect specific data point to an entire narrative, complete with names of people, dates, and references that never existed.

We can group LLM hallucinations into three broad categories:

1. Factual errors
   The model provides erroneous data: inaccurate historical dates, invented figures, incorrect descriptions of events, etc.
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2. Fabricated content
   When faced with a question it cannot answer, the LLM fills in the gaps with stories, bibliographies, articles, or papers that sound plausible but are false.
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3. Incoherent or contradictory output
   The model produces text that sounds good but contradicts the context, mixes up concepts, or simply makes no logical sense.
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In all these variations, the problem is the same: the user may trust something that is not true, because the output looks and reads as if it were.

If you are already experimenting with generative AI but are concerned about how much you can trust the answers, schedule a session with our experts at Nerds.ai to review your architecture and risks: contact us here.

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Why hallucinations occur in LLMs
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A key point for any technical team is to understand that hallucinations are not an isolated bug, but rather a natural consequence of how current LLMs function.
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1. LLMs predict text, not "truth"
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Transformer-based language models are trained to predict the next word in a sequence. They have no explicit goal of "truthfulness," only linguistic probability. If the sequence "seems" correct based on their training, the model chooses it.

In other words, an LLM is not optimized to distinguish between fact and fiction, but to produce text that fits statistically.
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2. Incomplete or biased training data
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Many LLM hallucinations stem from:

• Information gaps on very recent or highly niche topics.
• Biases in the dataset (certain sources or viewpoints being overrepresented).

When the model lacks sufficient or balanced data, it fills in the gaps with its best guess. This can mean inventing biographies, studies, laws, or conclusions that "look like" real texts, but are not.
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3. Overfitting and underfitting
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• An overfitted it can memorize fragments of the training data and repeat them out of context with unjustified confidence.
• An undertrained model does not capture enough patterns and produces erratic outputs.

Both extremes increase the risk of LLM hallucinations, because they reduce the ability to generalize correctly when faced with new inputs.
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4. Architecture and capacity limitations
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Not all models have the same capacity to represent context and complex relationships. A small model may handle general conversations well, but when faced with specialized tasks (legal, clinical, financial), it begins to "fill in the blanks" with generic formulas and subtle errors.

5. Text generation methods
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The way output is generated also influences hallucinations:

• Methods like beam search favor coherence and fluency, but do not guarantee accuracy.
• The high-temperature sampling increases creativity… and the risk of saying nonsensical things.

Adjusting temperature, top-k, top-p, and maximum length not only changes the style, it also changes the hallucination risk profile.
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6. Ambiguity and poorly formulated prompts
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If a prompt is vague, ambiguous, or contains contradictory instructions, the LLM interprets what it can. That room for interpretation is fertile ground for hallucinations:

• Overly broad questions.
• Lack of context.
• Mixed concepts.

A significant part of mitigating hallucinations in LLMs begins with the design of prompts and conversational flows.
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7. Lack of real-world grounding
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A raw LLM has no direct connection to:

• Verified knowledge bases.
• Real-time information.
• Internal company records.

Without grounding (for example, through document retrieval or internal APIs), the model cannot verify if what it says actually exists. It only “remembers” patterns learned up to its cutoff date.

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Real-world cases: when hallucinations leave the lab
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Talking about LLM hallucinations is not just theoretical. They have already caused incidents with economic, legal, and reputational impacts.
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Google Bard: an astronomical live error

At its launch, Bard claimed that the James Webb Space Telescope had taken the first images of an exoplanet. It was false. The data was quickly corrected, but the error in a public demo generated headlines worldwide and affected the product's perception.
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ChatGPT in a court case
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A lawyer in the U.S. used ChatGPT to generate a legal brief. The model cited court cases that simply did not exist. The judge caught it, the lawyer was sanctioned, and the episode became a global example of why you shouldn't blindly trust AI.
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Scientific models that invent papers
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Models oriented toward scientific domains have generated non-existent citations, authors, and studies. The risk here is not just reputational: in research, a hallucination can contaminate a chain of references and subsequent decisions.

These cases illustrate something important: even if the hallucination rate is “low” statistically, a single serious error in the wrong context can be unacceptable.
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If your use case is sensitive (finance, healthcare, legal, public sector), you need a serious evaluation and testing strategy. We can help you design that end-to-end pipeline. Write to us at Nerds.ai/contacto.

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How should we interpret LLM hallucinations?
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When a model hallucinates, it’s tempting to think “the AI lied.” But technically, there is no intent. It is more useful to interpret LLM hallucinations on three levels:
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1. Model limitation, not just a “bug”
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As long as LLMs aim to predict probable text, there will always be a margin for factual error. We can reduce it, but not eliminate it 100%.

Therefore, rather than expecting "zero hallucinations," it is reasonable to design systems that:

• Detect and reduce risk.
• Escalate to humans when the risk is high.
• Limit damage when they inevitably occur.
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2. Bias inherited from data
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Some hallucinations in LLMs are not random, but rather reflect systemic biases in the training data. For example:

• Associating certain negative traits with specific groups.
• Reproducing stereotypes or incomplete narratives.

Here, the hallucination is a symptom of a deeper problem with data quality and governance.
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3. User expectations
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In many incidents, the problem lay as much with the model as with expectations:

• Users who treat an LLM as if it were an infallible search engine.
• Decision-makers who put a model into production without guardrails or oversight.

An essential part of mitigation is educating on the technology's limitationsAn LLM is not a source of verified truth; it is a powerful generative tool that must operate within a controlled system.

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Strategies for mitigating LLM hallucinations

We cannot promise a "hallucination-free" model, but we can build systems where the risk is measured, bounded, and managed. The key lies in combining model decisions with product decisions.
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1. Grounding and knowledge retrieval
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The most effective technique today for reducing LLM hallucinations is Retrieval-Augmented Generation (RAG):

1. The system first searches through reliable sources (internal documentation, databases, controlled web).
2. Pass those texts to the model as context.
3. The LLM generates a response grounded in that evidence.

Advantages:

• Fewer fabrications, more concrete citations.
• Updated responses without retraining the model.
• Traceability: you can show where the information came from.

In our article on LLM evaluation and benchmarks we explain how to measure the impact of grounding on accuracy and factual truth metrics.
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2. Guardrails and output policies
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The guardrails are control layers around the model:

• Filters that block certain types of content.
• Rules that force the model to cite sources for sensitive data.
• Validators that check the output (another model, symbolic rules, or both).

Examples of policies to reduce LLM hallucinations:

• “Do not invent legal references; only use those provided in the context.”
• “Respond ‘I do not have enough information’ if no relevant documents are found in the repository.”
• “Never generate medical diagnoses; only provide general information and suggest speaking with a professional.”
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3. Fine-tuning to say “I don’t know”
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An important step is to train or fine-tune the model so that it prefers to abstain rather than invent. This can be achieved through:

• Supervised fine-tuning with examples where the correct answer is “I don’t know” or “I don’t have enough context.”
• RLHF where honest abstention is rewarded over confident invention.

This changes the risk profile: fewer “overconfident” responses, more transparency regarding uncertainty.
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4. Prompt design and conversational flows
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The prompting is not just a trick; it is part of product design. Best practices:

• Be explicit: “If you are not sure, state it clearly.”
• Reduce ambiguity: include context, examples, and expected format.
• Break complex tasks into steps (internal chain-of-thought + summarized response).

Furthermore, in user experiences where the LLM interacts with customers (for example, in conversational SEO or search assistants), it is advisable to align behavior with the search strategy. In our analysis on how to adapt SEO strategy for LLM-based search we delve into this new pattern: the model provides the answer, but based on verifiable and optimized content.
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5. Human oversight and escalation loops
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In critical domains, human-in-the-loop is not optional:

• Human review before publishing certain content (reports, legal communications, high-impact decisions).
• Automatic escalation to a human agent when the model detects low confidence.
• Periodic quality checks on interaction samples.

This oversight also creates a feedback loop: incorrect or questionable responses can be turned into new examples to refine the system.
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6. Production monitoring and user feedback
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Mitigating hallucinations in LLMs doesn't end on deployment day. You need:

• Dashboards with quality metrics (complaint rates, human corrections, discrepancies).
• Channels for users and agents to easily flag a response as incorrect.
• Processes to review incidents and apply fixes (new rules, adjusted prompts, model updates).
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This logic is similar to observability in traditional software, but applied to generative behaviors.
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If you want to move from an interesting prototype to a robust, monitored, and governed AI platformLet's talk. You can tell us about your case in this contact form.

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From prototype to product: the Nerds.ai value proposition
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In short, LLM hallucinations aren't going away tomorrow. But that doesn't mean you should give up on generative AI; it means you should treat it for what it is: a strategic capability that requires design, governance, and measurement.
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At Nerds.ai, we work with organizations that want to go beyond the demo and build AI solutions that:
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• Are designed from day one with guardrails, grounding, and monitoring.
• Are rigorously evaluated with business-relevant benchmarks, not just generic metrics, relying on practices like those we describe in our article on LLM evaluation.
• Integrate securely with your data, processes, and systems.
• Always put the user—and human judgment—at the center.
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Our value proposition is clear: we don't just connect a model; we design the entire experience and the scaffolding it needs to operate in production without becoming a risk.

If you are:

• Launching a customer assistant.
• Automating internal support with generative AI.
• Experimenting with report, content, or code generation.
• Rethinking your search and SEO strategy in a world dominated by LLMs.
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We can help you define a roadmap, select models, design workflows, implement guardrails, and set up a continuous evaluation framework.
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 Want to deploy generative AI without the fear of hallucinations? Take the next step and tell us about your use case at Nerds.ai. Let's design a solution together where the model's imagination works for your business, not against it.

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