A Framework for Measuring and Fixing AI Hallucinations

Hallucinations are the most frustrating kind of AI bug: you can’t reliably detect them, can’t always reproduce them, and worst of all, they’re not even technically bugs.

Table of Contents

Two Core Failure Modes: Intrinsic vs. Extrinsic
1. Intrinsic Hallucinations
2. Extrinsic Hallucinations
A Debugger’s Taxonomy
Data-Related Causes of AI Hallucinations
AI Hallucinations Mitigation Techniques
I Can Help You: One Hour to Clarity
Source Research Papers

They’re a design feature of how large language models generate answers: prediction over precision, fluency over fact. That’s why your AI assistant will write a fluent, confident answer… and then tell you Thomas Edison invented the telephone.

It’s not broken. It’s working exactly as intended… just not the way you want.

Researchers are blunt about hallucinations. In LLMs Will Always Hallucinate, and We Need to Live With This, the message is clear: hallucinations are here to stay. Our job isn’t to eliminate them. It’s to work around them, like around any persistent, unsolvable bug in production code.

After spending two weeks debugging a model that confidently told engineers to fix a problem by adjusting a nonexistent parameter, I realized something: hallucinations aren’t bugs. They’re features. This guide is for every engineering manager who’s ever felt the sting of confident nonsense… but still wants to ship. You’ll learn to recognize the major types of hallucinations, understand where they come from, and pick the right mitigation strategy so you can stop chasing the perfect answer and start designing systems that fail more gracefully.

We may be tempted to automate hallucination detection. Just build a classifier, throw in some examples, and let the system flag bad outputs. But reality’s not that cooperative.

According to The (Im)possibility of Automated Hallucination Detection in Large Language Models, even that is a moonshot. Unless your labeled dataset looks exactly like your production use cases, detection won’t generalize. Best case? The task is “only” hard. Worst case? It’s impossible.

In other words, no matter how many AI engineers you hire, you can’t catch hallucinations with automation alone. This is why detection must be paired with mitigation and why understanding the types of hallucinations matters so much.

Two Core Failure Modes: Intrinsic vs. Extrinsic

When a system fails, engineers instinctively ask: Was it a bad input or a bad interpretation? That same lens applies to hallucinations.

At a high level, AI hallucinations fall into two buckets based on how the model handles the data it should know.

Intrinsic Hallucinations

These happen when the answer is in the data (training set, fine-tuning set, or prompt) but the model fails to find it. Like a junior developer skimming the docs and still getting the implementation wrong, the model has the info but doesn’t connect the dots.

Extrinsic Hallucinations

Here, the model goes completely off-script. The LLM makes up information not in any source it was trained on or prompted with. LLMs would rather die (metaphorically, of course) than say ‘I don’t know.’ So they improvise, like a college student bluffing through an oral exam.

A binary split is useful, but such a taxonomy is too coarse when you’re trying to debug a live system. To build real guardrails, you need something more detailed.

A Debugger’s Taxonomy

That’s where the taxonomy from A Survey on Hallucination in Large Language Models comes in. The authors of the paper created a bug classification system. It tells you what went wrong, why it happened, and how to fix it.

The framework organizes hallucinations into practical categories that reflect their failure modes and suggest different mitigation strategies. It’s like moving from “something’s broken” to “the API request returned a stale cache due to a versioning mismatch.”

In the next section, we’ll break down that taxonomy.

Factuality Hallucinations

These are the most visibly wrong kind of hallucinations. Such a hallucination occurs when the model confidently states something objectively false. The hallucination is the AI equivalent of returning 2 + 2 = 5. It’s not an interpretation error. It’s a straight-up contradiction of the real world.

Factual Contradictions

Nothing surprising here. Factual contradictions happen when the model gets core facts wrong: people, dates, definitions, or historical events.

Entity-error Hallucinations

The model invents or mislabels a key entity. Ask AI who developed Kubernetes, and it might say “Google Cloud” instead of “Google”. A subtle mistake, but still a mistake. These errors are especially dangerous in factual applications like legal AI or medical assistants.

Relation-error Hallucinations

Here, the entities are correct, but the relationship between them is fabricated. For example, AI might claim “OpenAI was founded as a spin-off of DeepMind,” which incorrectly links two real organizations in a false narrative.

These errors erode user trust quickly because they’re often delivered with maximum confidence and zero hedge.

Factual Fabrications

These hallucinations don’t just get facts wrong. They invent things that can’t be verified at all. It’s like a junior developer who makes up a nonexistent API because it sounds like something that should exist. (Honestly, who hasn’t done that?)

Unverifiability Hallucinations

The model outputs something plausible but not backed by any known source. For instance, AI might claim that “Apple developed a secret AI chip called the A13-Z exclusively for internal Siri experiments.” That chip doesn’t exist. No official documentation, no news, no mention anywhere. The model synthesizes fiction using linguistic patterns.

Overclaim Hallucinations

The most dangerous hallucinations don’t look wrong. They look right. Overclaims are subtle. The model makes a sweeping statement that feels true but oversteps what the evidence supports. An example: “Rust has completely replaced C++ for all systems programming in big tech.” While Rust is gaining traction, the claim is overstated and not universally true.

These can be harder to spot because they often align with trends or opinions but lack nuance or caveats. You may overlook them while manually reviewing the data because they sound plausible, and if they support your biases, the answers just look right.

Faithfulness Hallucinations

These hallucinations are especially frustrating because they happen even when everything seems to be set up correctly. The prompt is clean, the context is relevant, and the model has no reason to get confused. But it does. It’s like giving a well-documented Jira ticket to a capable engineer and still getting the wrong output.

Instruction Inconsistency

You get instruction inconsistency when the model ignores or misinterprets your direct instructions. You ask AI to do one thing, and the model does something else entirely.

For example, you might say:

“Translate this Rust code into Python.”

And instead of translating, the model gives you an explanation of what the Rust code does, or worse, a critique of why Rust is better than Python.

The input wasn’t ambiguous. The model simply failed to follow directions. Instruction inconsistency is a major pain point for any workflow that depends on reproducibility or tightly scoped outputs: code generation, scripting, or even basic automation.

Context Inconsistency

These hallucinations occur when the model contradicts the information you gave it. You’ve handed AI the facts (explicitly and clearly), but the LLM fails to use them correctly. You may face the problem everywhere where context or past messages matter: chat systems, multi-step prompts, or chain-of-thought reasoning, where information needs to be held and applied across multiple interactions.

Imagine your prompt includes:

“The application uses MongoDB for data storage with the following data structures…”

And you even outline the schema and data format in detail.

Then the model responds with a PostgreSQL-compatible SQL query.

LLM didn’t forget. (AI can’t forget.) But it ignored the context and failed to anchor the response in the supplied ground truth. In production systems, these errors can lead to developer confusion, wasted debugging time, and even security concerns.

Logical Inconsistency

A frustrating kind of hallucination when the model contradicts itself within the same response. AI walks through steps that are individually correct, but the conclusion doesn’t follow or directly conflicts with the earlier logic.

For example:

“Divide both sides of the equation 2x = 10 by 2 to get x = 5.”

And then conclude:

“So the final answer is x = 4.”

These hallucinations usually appear in tasks involving reasoning chains (math problems, programming logic, or decision-making workflows). They’re tricky because the first 80% of the output builds trust. Only near the end does the logical rug get pulled out from under you. What’s even worse, when you build multi-step interactions, the mistakes in every step add up, and the output becomes more and more wrong.

In the paper this article is based on, the authors categorize the root causes of hallucinations into three buckets: data-related, training-related, and inference-related. It’s a helpful academic framing, but most of that taxonomy isn’t actionable in the real world of user-facing AI applications.

As an engineering manager, you’re not tuning neural weights or reengineering token sampling methods. You’re working with prompts, APIs, and maybe some fine-tuning datasets. That makes data-related causes the most important category to understand because it’s the one you can do something about.

Training-related issues? Mostly locked behind the model’s black box. Inference-level factors? Those live deep in the architecture, beyond your control. But data is your lever. Especially the data you pipe into the model via retrieval, fine-tuning, or structured prompting.

Imitative Falsehoods

Sometimes the AI gives you the wrong answer because it learned the wrong answer. If the training data or the prompt contains incorrect information, the model simply imitates what its creators provided.

This happens often in RAG systems. If your retrieval layer pulls flawed content from internal wikis, outdated docs, or noisy user forums, the model will faithfully repackage that bad data into a clean, confident response. Garbage in, garbage out (but with eloquence).

In these cases, the hallucination is a mirror of your source materials. If your knowledge base is polluted, the AI isn’t hallucinating. It’s telling the truth about your documentation debt.

Societal Biases

Bias in, bias out. Large language models inherit their training data’s social, cultural, and demographic biases. And unless that data has been carefully scrubbed or counterbalanced (spoiler: it hasn’t), the model will repeat those patterns.

Ask AI to describe two programmers in London (one from the U.S., one from Poland) and it might call the American an “expat” and the Pole an “immigrant.” Same job, same city, different framing. Not because it knows better, but because the internet talks that way.

These aren’t only ethical problems. Every bias is a product risk, especially in hiring tools, recommendation systems, or customer support agents. Left unchecked, biased outputs can damage trust and reinforce stereotypes at scale.

Long-Tail Knowledge

LLMs are great at what’s been said a million times. They struggle with what’s only been said once. The rarer the fact, the less likely the model knows it.

LLMs are great at high-frequency knowledge. Ask about HTTP status codes or Kubernetes pods, and you’ll get gold. However, ask about a niche open-source library from 2013 or an internal tool used by three teams in your org, and the model might start improvising.

Long-tail knowledge problems are hard to spot unless you’re an expert in the domain. That’s why subject-matter supervision or targeted fine-tuning can be so powerful: human expertise reinforces low-frequency knowledge that the base model never really mastered.

Up-to-Date Knowledge

Every model has a cutoff date, and without a retrieval mechanism, it lives in the past. Ask AI about last week’s latest iOS release or a security patch, and LLM may hallucinate an answer based on outdated priors.

Unless trained to do so, the model won’t say “I don’t know.” It’ll guess. And if it’s wrong, it’ll be wrong with confidence. Retrieval-augmented generation (RAG) is so critical for real-time systems. Without fresh context, even the best-tuned models will reach for patterns that no longer apply.

AI Hallucinations Mitigation Techniques

Before you can reduce hallucinations, you have to understand what kind you’re getting and how often.

Too many teams jump straight into applying techniques like RAG, fine-tuning, or prompt engineering without first building a solid evaluation framework. That’s like debugging a flaky service without knowing what “flaky” means.

Hallucination Mitigation Starts With Measurement

One of the best pieces of advice came from the MLOps Community Slack, where practitioners are tackling problems in real-world systems. As Misha Iakovlev said:

“At least at first, I would go with separate performance metrics by class, rather than a (weighted) average. This gives a more complete picture, and shows you what types of cases are handled well, and what are not.”

Start by tracking the types of hallucinations you see: factual errors, context drift, overclaims, etc. Keep them separate. Don’t flatten everything into a single metric yet. Before you can prioritize the right fixes, you need visibility into which bugs show up and where.

Elena Samuylova, another voice in the MLOps Community, put it perfectly:

“Designing evals is def a lot more art in addition to science… Even a small bit of real data helps ground your assumptions. You can assume topics / types of questions will be similar and generate more along those lines.”

When you don’t have enough real-world usage yet, start with synthetic. Build test sets that reflect your best guess about common use cases. Then, refine as real users interact with your system.

Elena recommends building at least three types of eval sets:

Correctness sets for common questions, using broad synthetic examples
Golden sets of hand-checked, high-priority examples with domain experts
Edge case sets for refusal scenarios, temporal logic, and known pain points

The bar for testing depends on your risk tolerance, but don’t wait for perfection. Get to “good enough,” ship something, then log and iterate. Real data is always the best teacher.

(If you’re not already there, join the MLOps Community Slack. It’s one of the few places where people share this kind of boots-on-the-ground experience in public.)

How to Mitigate Hallucinations (When You Can’t Eliminate Them)

Once you’ve measured the kinds of hallucinations you’re facing, you can start selecting the right mitigation strategies. There’s no silver bullet, but a mix of engineering discipline and model-guidance techniques can significantly reduce the worst cases.

The best methods depend on the failure mode. Here’s a practical breakdown based on three major types of hallucinations:

Input-Conflicting Hallucinations

These happen when the model misunderstands or contradicts the input: your prompt, task description, or source content.

Prompt Engineering

The simplest and often most overlooked fix starts with better prompting. Chain-of-thought prompts, for example, encourage the model to reason step-by-step before committing to an answer. The structure discourages wild guesses and gives you visibility into where things go off track.

Another tactic: include clear, assertive system-level instructions that explicitly discourage invention. A well-scoped prompt often does more to reduce hallucinations than a dozen lines of post-processing logic.

Task-Specific Training

If you’re fine-tuning, even at a small scale, you can train models on examples specifically designed to reinforce faithfulness. The typical use cases when small-scale fine-tuning may work well are summarization tasks where the summary must strictly mirror the source, or question answering where the answer is always provably present in the input.

Context-Conflicting Hallucinations

These occur when the model drifts away from the provided context or introduces internal contradictions within a single response.

Self-Reflection

The Self-Reflection strategy prompts the model to review its output for logical flaws, contradictions, or gaps in reasoning. After an initial response, the system asks the model to critique or revise what it just wrote, flagging issues before the output reaches the user.

It’s a lightweight way to catch inconsistencies without needing an external verifier. And because the technique uses the model’s capabilities, it’s relatively easy to integrate into existing workflows. You’re essentially adding a second layer of reasoning: one focused not on answering, but on reviewing.

Fact-Conflicting Hallucinations

These are the most visible and damaging errors: when the model confidently states something that isn’t true.

Cross-Model Consensus and Debate

One promising technique involves querying multiple models (or multiple versions of the same model, or the same model used multiple times) and having them converge on a shared answer. When their outputs differ, the system triggers a reconciliation step. The “debate” format tends to filter out weaker or hallucinated claims.

A variant of the technique involves setting up a dialogue: one model generates a claim, and another questions it. The adversarial structure often surfaces factual gaps and forces clarification.

Retrieval-Augmented Generation (RAG)

Tethering the model to an external source of truth is one of the most powerful ways to reduce factual hallucinations. In a typical RAG pipeline, the model queries a document store or search index for evidence, then generates its response based on that retrieved content.

Advanced systems even close the loop: verifying the output against the retrieved evidence and revising as needed until the result is grounded. If you want to sell the idea to a top-level executive, say you implement real-time fact-checking during generation.

In some setups, post-processing tools scan outputs for unsupported claims and retroactively align them with external sources. Whether you do this before or after generation, the key idea is the same: don’t let the model rely solely on memory. Anchor the response generation in something real.

Hallucination Mitigation in Retrieval-Augmented Generation (RAG)

While RAG is often promoted as a solution to LLM hallucinations by grounding generations in external documents, Jason Liu argues that most RAG systems still hallucinate. Mitigating hallucinations in RAG, according to Liu, requires treating the retrieval component itself as the primary problem, not the generation.

Liu lays out a step-by-step framework to systematically improve RAG systems and reduce hallucinations, focusing on practical engineering principles:

Start with Synthetic QA Data

To test retrieval quality in isolation from generation noise, Liu recommends:

Generating synthetic questions for every text chunk in your knowledge base.
Evaluating whether those chunks are correctly retrieved using recall and precision metrics.

Starting with synthetic data gives a ground-truth baseline. Surprisingly, Liu found that full-text search often outperformed embedding-based retrieval. The takeaway: if your retrieval fails on synthetic data, your generation has no chance of staying grounded.

Metadata for Query Understanding

Hallucinations often arise when user queries contain implicit constraints (e.g., “recent” events), which neither full-text nor vector search can resolve without metadata.

Mitigation strategy:

Extract and index metadata (e.g., dates, sources, document type).
Perform query understanding to interpret temporal or source-based constraints.
Expand queries with relevant metadata to improve grounding.

Metadata should help you avoid hallucinations like returning outdated or irrelevant content due to unparsed context in the query.

Use Hybrid Retrieval (Text + Embeddings)

Rather than relying on one retrieval type, Liu recommends combining:

Full-text search for speed and keyword precision
Vector search for fuzzy, semantic matching

By fusing both (ideally in one unified database), you improve recall while reducing reliance on guesswork from LLMs trying to “fill in the gaps.” We have been using full-text search for years, and we don’t need to throw away that experience. Instead, we build a hybrid setup to reduce hallucination by ensuring relevant content is more likely to be retrieved in the first place.

Implement Clear Feedback Loops

Generic “thumbs up/down” buttons are too vague to identify hallucination-related failures.

Instead:

Ask specific questions like “Did we answer your question correctly?”, “Which source documents are relevant?”, etc.
Use the structured feedback to label hallucinated vs. accurate outputs

Build evaluation datasets from user feedback to benchmark future changes. Precise labeling allows you to cluster problematic examples and train or tune models to avoid recurring errors.

Cluster Topics and Capability Gaps

By grouping failed queries:

You can identify clusters of hallucination-prone topics (e.g., recent updates, troubleshooting steps).
You can find capability gaps: categories of questions (e.g., comparisons, deadlines) that always fail.

After grouping the problems, calculate which group has the biggest impact (the most common group with the highest number of errors) and prioritize system improvements by topic. You can even build specialized subsystems for certain categories (e.g., step-by-step reasoning modules).

I Can Help You: One Hour to Clarity

Hallucinations are trust killers. And most engineering teams don’t realize they have a hallucination problem until it’s already cost them time, money, or credibility.

If you’re building with AI and want to catch these issues before they show up in production, I can help. In just one hour, I’ll diagnose your system’s failure points, and within a week, you’ll have a clear, custom roadmap to stop hallucinations for good.

I spent two weeks trying to fix a ghost parameter. You shouldn’t have to. If your AI systems feel like they’re gaslighting you, let’s talk.

Source Research Papers

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