TL;DR:
Hallucinations in generative AI—fabricated, inaccurate information—can wreak havoc in logistics, retail, transportation, and healthcare operations. Techniques like query expansion, reranking, embedding adapters, and model tuning can significantly reduce these errors, improving forecasting, routing, and inventory decisions.

Generative AI Hallucinations: A Logistics Nightmare and How to Stop Them

Introduction: What Are AI “Hallucinations”?

Imagine asking an AI for advice on optimizing your delivery routes or forecasting next month’s product demand, and it responds with confident-sounding nonsense. In the world of generative AI, this phenomenon is known as a hallucination. When a language model hallucinates, it generates information that seems accurate but is actually false. In other words, the AI is essentially making things up – not out of malice, but because it doesn’t know any better given its training and input.

For data and operations managers in logistics, retail, transportation, and healthcare, these AI hallucinations can quickly become a nightmare. Decisions in supply chain and fulfillment rely on accurate, trustworthy data. A fabricated route suggestion or a bogus demand forecast from an AI could lead to costly errors. In high-stakes environments like healthcare and critical supply chains, such AI slip-ups are a major concern. Let’s briefly consider why:

• Misinformed Planning: In demand forecasting, a hallucinating AI might invent a trend or sales figure that isn’t real. Acting on that could cause overstocking or stockouts, hurting your business.
• Operational Errors: For transportation routing, an AI could “hallucinate” an optimal route that doesn’t exist (or ignore a real-world constraint), leading to delays or lost shipments.
• Loss of Trust: If your fulfillment team gets AI-generated insights that turn out to be wrong, they’ll lose confidence in the tool. You end up with wasted time double-checking AI outputs, negating any efficiency gains.

The good news? We can tame these hallucinations. Researchers and AI engineers have developed techniques to make generative AI more grounded and reliable. In this post, we’ll explore four practical approaches – query expansion, reranking, embedding adapters, and model tuning – and see how each can curb AI hallucinations in real-world logistics operations. We’ll keep it casual and jargon-free, focusing on how these methods help deliver more accurate insights for transportation routing, demand planning, inventory control, and healthcare supply chains.

Query Expansion: Asking Better Questions to Get the Right Answers

One straightforward way to get more reliable AI answers is to feed the AI better questions (or queries). Query expansion is like giving your search engine or AI assistant extra clues to work with. Instead of taking a user query at face value, the system broadens or clarifies it by adding related terms, synonyms, or context. The goal is to ensure the AI finds all the relevant information it needs, rather than coming up empty and hallucinating an answer.

Think of it this way: if you search for “latest delivery optimization methods”, a basic system might miss content that uses different wording. Query expansion could automatically include terms like “current route planning strategies” or “new logistics efficiency techniques” alongside your original query. By enhancing the query with synonyms or related phrases, the retrieval system can find documents that are closely related even if they don’t use the exact words from your question. In essence, it casts a wider net to catch the information you need.

How does this reduce hallucinations? If the AI has access to relevant, real data from an expanded query, it doesn’t have to fill gaps with guesses. For example, in demand planning for retail, you might ask an AI, “Will we have stockouts for winter coats next month?” A naive AI could miss that “next month” is December (peak season) and just speculate. But a smart system using query expansion might reformulate the query behind the scenes to include terms like “holiday season demand for winter coats” and pull in actual sales history and weather data. Armed with that real data, the AI can give a grounded answer (e.g. pointing out last year’s December spike in coat sales) instead of a hallucinated one.

In transportation routing, query expansion can ensure the AI considers all relevant factors. A query like “optimize route A to B” might be expanded with context like “traffic patterns,” “road closures,” or “fuel-efficient routes.” This helps retrieve up-to-date traffic reports or prior route performance data. The AI, seeing that information, is less likely to invent a shortcut that saves time (which could have been a hallucination) and more likely to recommend a route based on actual data (e.g. known traffic conditions and fuel use statistics).

In short, query expansion helps the AI help you. By asking better, richer questions of your data, it reduces the odds that the AI will stray into fantasy. It’s a bit like having an experienced colleague who says, “I know what you’re really asking, let me double-check all related info so I don’t steer you wrong.” This technique is a simple yet powerful guardrail to keep AI-generated insights fact-based and relevant in logistics operations.

Reranking: Picking the Best (and Most Real) Answer

Even after expanding a query, an AI system might retrieve a bunch of possible answers or documents – some spot-on, some not so much. This is where reranking comes into play. Reranking means reordering the search results or candidate answers based on how relevant and reliable they are, after the initial retrieval. It’s like saying, “Okay, I’ve got 10 possible pieces of info – now let me sort them so the best ones are on top.”

Why does this matter for hallucinations? Because one major cause of AI hallucinations is when the AI latches onto irrelevant or low-quality information. If an AI’s pool of context includes unrelated or misleading content, the model might incorporate those into its answer, essentially mixing truth with fiction. By using reranking, we prioritize the most relevant, trustworthy information and filter out the noise before the AI formulates a response.

Here’s a practical example in a fulfillment management scenario: Suppose your AI assistant pulls data about warehouse inventory to answer “Can we fulfill a surge of 500 orders for item X next week?” The system might retrieve several pieces of information – recent inventory levels for item X, supply delivery schedules, even a document about a different item by mistake. Reranking algorithms (often using advanced models like cross-encoders) will score each piece by relevance to your question. They might determine that the inventory levels and supply schedules are highly relevant, while that document about a different item is not. By reordering and taking the top results, the AI ends up considering only the pertinent facts (current stock = 400 units, next shipment arriving in 3 days, etc.). The final answer is then based on solid ground, reducing the chance of a hallucination like “Sure, we have plenty in stock” when in reality we don’t.

In transportation or route planning, reranking is equally valuable. Let’s say an AI system is helping schedule medical supply deliveries for a healthcare network, and you ask it for the fastest route including all critical drop-off points. The system’s first pass might retrieve a mix of route options: some current, some outdated, some missing certain stops. A reranker will sort those options, pushing routes that cover all required points and use current traffic data to the top. Essentially, reranking acts as a quality gatekeeper, ensuring the AI uses the best available evidence. By filtering out irrelevant info (which could have led to a confused or made-up answer), reranking helps the AI stick to the facts and provides an optimal route plan you can trust.

To put it simply, reranking is about making the AI “read” the right materials before it speaks. It’s an extra step that says: “Double-check that source. Is it the right one? If not, ignore it.” This way, whether it’s for demand forecasting, inventory control, or delivery routing, the AI’s answers stay grounded in reality.

Embedding Adapters: Aligning AI with Your Domain

The term “embedding adapter” sounds technical, but the concept is approachable. It’s basically a smart tweak to how the AI understands your queries and data. Under the hood, AI models turn words into numbers (vectors) — that’s what we call embeddings. However, a generic embedding might not perfectly capture the nuances of your company’s logistics data or industry lingo. An embedding adapter is a small neural network module that fine-tunes this embedding process. Its job is to align the AI’s understanding of your query with the way your relevant documents are structured and phrased.

Think of it as an interpreter or adapter that fits between your question and the AI’s brain: it adjusts the “language” of your query so that it matches how your company’s data is indexed. By doing so, the AI is more likely to retrieve the correct facts, rather than near-misses that could lead to confusion. In plain terms: an embedding adapter helps the AI search its knowledge base in a way that’s tailor-made for your context.

How does this reduce hallucinations? By improving the alignment between questions and the knowledge base, the AI is less likely to get confused or return irrelevant info. In a sense, it narrows the interpretation gap. If your query lines up exactly with how the data is stored, there’s less wiggle room for the AI to wander off-topic.

Consider inventory control at a large retailer as an example. Say you ask, “Do we have SKU 12345 in stock at Warehouse C and how many sold last week?” A generic AI without an embedding adapter might not fully grasp what “SKU 12345” refers to, and could retrieve documents for similar product codes or mix up warehouses – leading it to hallucinate an answer like “SKU 12345? Yes, plenty in stock,” when in reality it was looking at the wrong item. Now introduce an embedding adapter that’s been fine-tuned on your product catalog data. It “knows” that SKU 12345 is a specific item and how stock records are formatted. The adapter transforms your query into a vector that precisely points to the records for SKU 12345 in Warehouse C. The AI then retrieves the exact sales and stock data needed. The result: a factual answer (“SKU 12345 has 200 units at Warehouse C, and 50 were sold last week”) with no invented info. By tweaking the query embeddings to better match the data in your vector database, the embedding adapter ensures the AI isn’t led astray.

In a healthcare supply chain scenario, an embedding adapter can be a lifesaver for accuracy. Medical inventories and terminology are complex – e.g., “PPE size L” vs “PPE Large” vs product codes for N95 masks. If an AI misaligns a query about protective equipment stock, it might pull wrong data and give a hazardous false assurance. But with an embedding adapter trained on the hospital’s procurement data, the AI will more reliably match “PPE size L” with the correct item in the database. This means when you ask, “How many large N95 masks are available across our facilities?”, the AI won’t confuse “large” as just a descriptive adjective – the adapter has tuned it to find the exact item category. The answer you get will be based on real inventory counts, not a hallucinated guess.

In summary, embedding adapters act behind the scenes to custom-fit the AI to your domain data. By doing so, they dramatically cut down on hallucinations that arise from misunderstandings. Your AI becomes more like a knowledgeable insider, fluent in your company’s “data dialect,” whether it’s retail SKUs, logistics codes, or medical supply terminology. The payoff is more accurate insights and fewer unwelcome surprises from a rogue AI imagination.

Model Tuning: Training the AI on What Really Matters

The last (and probably most powerful) technique in our toolkit is model tuning – essentially, training or fine-tuning the AI on your specific domain knowledge. Out of the box, a large language model (LLM) has learned a bit of everything from the internet. That’s impressive, but it also means it might not know the details of your logistics operations or it might have learned some facts incorrectly. Fine-tuning the model on relevant data teaches it the right patterns and facts for your needs, which can greatly reduce its tendency to make things up.

Think of an untuned AI model as a new employee who’s read every textbook but never worked in your company. They have general smarts, but if you ask them about your inventory process or your regional delivery constraints, they might speculate or give a textbook answer that doesn’t apply. Model tuning is like giving that employee on-the-job training and access to your internal manuals. Afterward, they perform much better on company-specific questions and are less likely to bluff when they don’t know something.

In technical terms, fine-tuning adjusts the LLM’s learned patterns to align with the nuanced, factual information specific to your domain. For example, you could fine-tune a model on historical supply chain data, route logs, or Q&A pairs about logistics. With that training, the model “remembers” actual facts and trends from your business, rather than relying solely on its generic training. The effect is dramatic: domain-specific, fine-tuned models can greatly reduce hallucinations – those wildly incorrect responses – that you sometimes get from a generic AI.

Let’s illustrate with a demand forecasting use case. Suppose you manage inventory for a chain of pharmacies and you’re using an AI assistant to project next quarter’s demand for flu vaccines. A vanilla generative model might only know general flu season trends or global data; it could hallucinate an answer like “demand will double next quarter” without any basis in your locale or past data. Now, if we fine-tune the model on your pharmacy’s historical sales, regional health data, and maybe even some expert adjustments from past forecasts, the AI will have seen real patterns. It might recall that, say, in your data flu shot demand increases by 30% in Q4 in urban stores but stays flat in rural ones. Therefore, its forecast will be grounded in those learned patterns. Rather than guessing, it could output a nuanced prediction, e.g., “Expect a ~30% rise in flu vaccine demand in city locations, based on last year’s trend and population data,” backed by what it learned during fine-tuning.

In transportation and fulfillment contexts, a fine-tuned model similarly shines. Picture a logistics AI that’s been fine-tuned on years of shipping data: it has essentially learned the typical transit times, the effect of weather on routes, warehouse processing times, etc. Ask this tuned model for a delivery estimate or a bottleneck prediction, and it’s far more likely to give you an accurate answer (because it has seen similar situations in the fine-tuning data). In contrast, an untuned model might not know these specifics and could hallucinate an unrealistic delivery time or fail to foresee a common delay. Fine-tuning instills the ground truth of your operations into the model’s “memory,” so it doesn’t have to fabricate answers.

It’s worth noting that fine-tuning can be resource-intensive – it’s like specialized training for the AI – so it’s often a later step after trying things like query expansion or reranking. But for many organizations, this investment is worthwhile. By the end of a fine-tuning process, the AI model becomes a bespoke assistant for your team: it speaks your industry’s language and has your data’s facts at its fingertips. The result is an AI that you can trust with critical tasks like forecasting and route planning, because it’s far less prone to the kind of off-base responses that untrained models might give.

Conclusion: Safer AI for Smarter Logistics

Generative AI is a powerful tool for logistics and supply chain management – from forecasting demand and optimizing routes to managing inventory and streamlining healthcare supply chains. But as we’ve discussed, unchecked AI hallucinations can derail these benefits by injecting false information into the decision-making process. Understanding this challenge is the first step to solving it. The next step is applying the right techniques to keep your AI on the rails:

• Query Expansion helps ensure the AI has the right context by broadening queries to fetch all relevant data (so it doesn’t answer out of thin air).
• Reranking lets us filter and prioritize information, so the AI’s answers are built on the most pertinent and factual pieces of data (reducing the chances of a random tangent turning into a false statement).
• Embedding Adapters align the AI’s understanding with your data, acting like a custom lens that makes sure queries and documents truly match up (preventing those misunderstandings that lead to made-up answers).
• Model Tuning (fine-tuning) gives the AI deep training on your domain, equipping it with real knowledge of your operations so it doesn’t need to guess in the first place.

None of these techniques involve magic – they’re extensions of good old-fashioned data quality and validation, brought into the AI era. By investing in these approaches, Runink and companies like it help data and operations managers get AI tools that are reliable co-pilots rather than loose cannons. The end result is that you can confidently use generative AI to guide digital transformation in logistics, without losing sleep over whether the latest recommendation is real or a hallucination.

In a fast-paced logistics environment, trust is everything. With query expansion, reranking, embedding adapters, and model tuning in your playbook, you can trust that your AI is working with you and with the facts. That means better forecasts, smoother fulfillment, and ultimately a more resilient supply chain – no nightmares required.