Some examples in Supply Chain
Some words of introduction on Prompting and giving context
In our previous post, we illustrated how you can help an LLM by giving it context—what we refer to as context-prompting. Some people asked us, rightfully so, whether this actually works. Are the answers any better? That’s a good question. The simple answer is: yes, they are. However, while giving context is essential, having a proper framework in place to validate every answer is also a must.
Let’s try to answer this question first from a theoretical perspective before we dive into some practical examples. There is strong theoretical and empirical evidence that providing AI with better context significantly improves its performance. This is grounded in several areas of research, including natural language processing (NLP), cognitive science, information retrieval, and reinforcement learning. Here’s why structured context matters and how it aligns with both theoretical principles and experimental findings:
1. The Information Theory Perspective
Claude Shannon’s Information Theory (1948) explains that the more structured and relevant information we provide, the less uncertainty there is in an output. AI systems—especially Large Language Models (LLMs) like GPT—operate on this principle: when an AI model is given vague or insufficient input, it must “guess” more based on probabilistic inference, leading to lower accuracy or more generic, hallucinated, or misaligned responses. When we provide an AI with well-structured, high-quality context, it can reduce uncertainty and generate more precise, relevant, and insightful outputs. In practical terms, this is why prompt engineering and context injection are essential when interacting with AI models: they reduce ambiguity and allow the model to focus on relevant information.
2. Empirical Evidence from AI Research
Several recent studies confirm that providing AI with more structured, domain-specific context improves performance. Key findings include: Few-Shot & Chain-of-Thought Prompting (Brown et al., 2020; Wei et al., 2022). The GPT-3 paper (Language Models are Few-Shot Learners, Brown et al., 2020) demonstrated that LLMs perform significantly better when provided with contextual examples rather than just raw questions. The Chain-of-Thought (CoT) prompting paper (Wei et al., 2022) found that breaking down problems step-by-step with structured context leads to better reasoning and accuracy in complex tasks like math and logical reasoning.
When we expect AI to generate valuable output, we must first structure the inputs properly. If an AI is fed disorganized, context-poor data, its output will be far weaker than if it’s given structured domain knowledge and clear problem framing.
3. Providing Industry and Company-Specific Data
One of the biggest breakthroughs in AI accuracy has been Retrieval-Augmented Generation (RAG)—where an AI retrieves relevant documents, structured data, or domain-specific knowledge before generating an answer. Recent research found that AI models perform significantly better when they retrieve structured context before generating answers rather than relying purely on pre-trained knowledge.[1]
AI’s effectiveness depends on its ability to access high-quality context, either through structured knowledge bases, domain-specific fine-tuning, or real-time information retrieval. It’s as simple as that. This is why, in our opinion, investing in structuring data for AI models is the right strategy. Instead of relying on generic AI outputs, organizations that provide structured knowledge graphs, curated datasets, and well-framed business questions will see vastly superior results.[2] It really is one of the three important ingredients that any company needs to have in place to fully benefit from AI (next to a Datagraph and AI-intelligence).
Back to Supply Chain: How This Applies to Business AI
In line with our article about context and the guide on AI in Supply Chain (download here), we gave the AI a context-rich prompt before starting our line of questioning, in a so-called chain of thought. This is what we provided first:
Back to Supply Chain: How This Applies to Business AI
In line with our article about context and the guide on AI in Supply Chain (download here), we gave the AI a context-rich prompt before starting our line of questioning, in a so-called chain of thought. This is what we provided first:
Prompt to give context:
We operate in the automotive sector with a company size of 50,000+ employees and annual revenues exceeding €50 billion. Our business model is a combination of manufacturing and B2B/B2C distribution, producing vehicles, components, and aftermarket services for both commercial and individual consumers.
We are subject to key regulations and compliance needs such as EU vehicle safety and emissions standards (Euro 7), supply chain due diligence regulations, sustainability directives (EU Green Deal), and international trade laws (WTO agreements, Brexit-related regulations).
Additionally, we comply with ISO 9001 for quality management and ISO 14001 for environmental management.
We serve markets in Europe as our primary region, with strong operations in Germany, France, Italy, Spain, and the UK. Beyond Europe, we have a global supply chain with key suppliers in Asia (China, Japan, South Korea), North America (US, Mexico, Canada), and emerging markets in South America and Africa.
This requires us to manage a highly complex and interdependent supply chain, balancing just-in-time (JIT) production, multi-tier supplier risk management, geopolitical uncertainties (US-China trade relations, EU sanctions), and sustainability challenges such as reducing Scope 3 emissions in our logistics network.
This realistic and structured example ensures that the AI model can retrieve and generate relevant, context-aware insights—whether for AI-driven supply chain optimization, risk management, or regulatory compliance analysis. But let’s focus on resilience.
Once given this context, we can start asking questions to the LLM of choice, and here comes the impressive part..
Example real-life data Supply Chain Resilience
This is already good stuff but we have to look beyond the actual output. The important lesson in my opinion is that the system is capable of generating this kind of output based on very limited information. It is still a test.
Let’s take it one step further. What if we give AI additional information about the type of product (product group level) linked to the supplier base? This happened.
Conclusions
Now, this AI had clear constraints, objectives, and a defined problem space, allowing it to generate a more precise, strategic, and actionable answer. This is why companies that structure their data and questions effectively will extract more value from AI. The theoretical and empirical evidence overwhelmingly shows that AI delivers better results when provided with structured context. AI is only as good as the knowledge and structure we provide it. Treating AI as a black box leads to inconsistent results, but providing domain-specific, structured context makes it vastly more effective. The real competitive edge in AI adoption isn’t just about using the latest models, but about structuring the right data, knowledge, and business questions. And it goes beyond prompt engineering—it’s about knowledge engineering. AI needs structured information, just like humans do.Want to Know More?
In this guide we present some examples that give you insight in the value of giving context, a strategy called In-Context-Learning or Context Prompting. Start simple and take it from there, with adding data, context and training your AI.
