Imagine this scenario: You've just bought a new smart plug, and you're excited to integrate it with your Bosch Smart Home system. Instead of digging through the manual, you decide to ask your friendly AI chatbot for help.
"Can I add my TP-Link smart plug to the Bosch Smart Home system?" you ask.
The AI confidently responds: "Yes! Here's how to do it: First, open the Bosch Smart Home app and navigate to Settings. Then tap 'Add Device' and select 'Third-Party Integrations.' Choose 'TP-Link' from the list, enter your TP-Link credentials, and your smart plug will appear in your device list within minutes!"
Excited, you follow these steps... only to find that none of these options exist in your app. There's no "Third-Party Integrations" menu. The TP-Link option isn't anywhere to be found. You've just wasted 20 minutes following instructions for a feature that doesn't exist.
The AI hallucinated the entire procedure.
The Frustration is Real (and Justified)
If you've experienced something like this, your frustration is completely understandable. You trusted the AI to provide accurate information, and instead, it confidently fed you fiction. This isn't just annoying—it's a waste of your time and can erode trust in AI assistance altogether.
The problem isn't that the AI is deliberately lying to you. It's doing what large language models (LLMs) do: generating plausible-sounding text based on patterns learned during training.
The Solution: Ground Your Questions in Actual Data
Here's a better approach that reduces the likelihood of
hallucinations: instead of asking the LLM to answer from its training knowledge, give it the actual source material and ask it to answer based on that.
Let's revisit our smart home scenario with this improved approach:
Poor approach (what we did before):
"Can I add my TP-Link smart plug to the Bosch Smart Home system?"
Better approach:
"Here's the URL to the Bosch Smart Home user manual: [URL]. Based on the information provided in this manual, can I add my TP-Link smart plug to the system? If yes, please explain how based on what the manual says."
Or, even better, upload the PDF of the user manual and ask:
"I've uploaded the Bosch Smart Home user manual. Based on the information in this document, can I add my TP-Link smart plug to the system? If the manual explains how to do this, please provide the steps exactly as described in the document. If the manual doesn't mention this capability, please tell me that as well."
This simple change transforms the task from "recall and generate" to "read and extract"—a task that LLMs are much better at and far less likely to hallucinate during.
Why Does This Work?
When you provide source material, you're forcing the LLM to ground its response in concrete, verifiable information rather than relying on its compressed training knowledge. The model can directly reference the manual, quote relevant sections, and acknowledge when information isn't present.
Without this grounding, the LLM is essentially working from "imperfect memory" and might produce something which is similar to the training data but not necessarily correct. This is because LLMs don't store information like a database; they compress it into mathematical patterns (parameters) during training.
Think of it like this: Imagine you read thousands of books, then someone asks you specific questions about them years later. You might remember general themes, common patterns, and typical procedures. But would you remember the exact steps for a specific task in a specific product manual? Probably not. You might instead recall similar procedures from other products and unconsciously blend them together, creating a plausible-sounding but ultimately incorrect answer.
This is essentially what happens when an LLM hallucinates. The model has seen patterns like "smart home integration procedures" thousands of times in its training data. When asked about Bosch Smart Home specifically, it generates text that follows the typical pattern, but it's not retrieving the exact Bosch documentation. In other words, the LLM is creating a statistically plausible response based on compressed representations of similar procedures.
Enter Context Engineering
This brings us to an increasingly important technique called context engineering: the practice of providing relevant information in the prompt context to guide the model's responses.
What is context engineering?
Context engineering is the process of carefully curating and including relevant source material, data, or documentation directly in your conversation with an LLM, for example, by pointing the LLM to relevant URLs or by uploading relevant documents in your chat. By doing this, you provide the exact information needed to answer the question accurately instead of relying on the model's training knowledge (which is compressed and imperfect).
How does it work?
When you provide context (like a user manual, documentation, or specific data), the LLM can:
- Reference concrete information rather than generating from training patterns
- Verify claims against the provided material
- Acknowledge gaps when the context doesn't contain relevant information
- Quote or paraphrase directly from authoritative sources
The model essentially switches from "generation mode" to "comprehension and extraction mode". It's the difference between asking someone to tell you about a movie they saw years ago versus asking them to summarize a movie while they're currently watching it.
Technically, context engineering reduces the likelihood of hallucinations through the combination of following factors:
- Anchoring effect: The model's attention is focused on the provided context rather than wandering through its training knowledge
- Verification mechanism: The model can cross-reference its generated response against the source material
- Scope limitation: By grounding the response in specific documentation, you limit the scope of possible answers to what's actually documented
- Transparency: It becomes easier to spot hallucinations because you can check the model's claims against the source
Practical Tips for Effective Context Engineering
Here are some concrete ways to implement context engineering in your daily use of LLMs:
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Provide source documents when asking factual questions
- Upload PDFs of manuals, reports, or documentation
- Share URLs to official documentation or specifications
- Paste relevant text excerpts directly into your prompt
-
Be explicit about the grounding requirement
- Use phrases like "based on the provided document" or "according to this source"
- Ask the model to quote or reference specific sections
- Request that the model acknowledges if information isn't in the provided context
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Verify and cross-reference
- When accuracy is critical, ask the model to cite specific page numbers or sections
- Cross-reference the LLM's answer with the original source yourself
- Request that the model distinguish between what's explicitly stated and what it's inferring
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Frame questions to encourage grounding
- Instead of: "How do I configure feature X?", try: "I've attached the configuration guide. Please explain how to configure feature X based on the steps outlined in Section 4 of this guide."
Bottom Line
LLM hallucinations aren't a sign of a broken technology, they're a natural consequence of how these models work. By understanding that LLMs compress information during training rather than storing exact facts, you can work with their strengths and mitigate their weaknesses.
Context engineering is your primary defense against hallucinations. By grounding your questions in actual data — whether that's a user manual, documentation, or any authoritative source — you transform the LLM's task from unreliable recall to reliable extraction.
The next time you're tempted to ask an AI a factual question, pause and think: Do I have a source document or URL I can provide? That simple step could save you from following a set of instructions to integrate your smart plug with a menu that doesn't exist.
After all, even the smartest AI can't accurately recall what it never properly "remembered" in the first place. But give it the right context, and it becomes a powerful tool for extracting insights from information you already have.
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