Two years ago, everyone was racing to build the biggest model. More parameters. More training data and more compute. The assumption was simple – a bigger model would be a better model. But something shifted in 2025 and became very clear by 2026. The teams getting the best results were not always the ones with the biggest models. They were the ones who got really good at what they put into those models before each response – which is the core of context engineering in AI. That skill has a name now. This is known as context engineering in AI, and it is rapidly turning into one of the most valued skills in the domain. This article will describe what happened, why this matters, and how context engineering in AI is revolutionising product development on top of large language models at the moment.
The Model Is Not the Problem Anymore
Here is a fact that surprises a lot of people. Most of the top foundation models available today – GPT-4o, Claude Sonnet, Gemini 1.5 Pro – can all produce excellent output on the same task. The raw capability gap between them has shrunk. So the question is no longer “which model is best?” The real question is now “who is feeding that model the right information at the right time?”
This shift is what makes context engineering in AI so important. A great model with poor context gives weak results. An average model with excellent context often beats it. Consequently, the competitive edge has moved away from model selection and toward context design – what you put in, how you structure it, and when you give it to the model.
What Changed When Context Became the Bottleneck
Think about it this way. A model on its own knows a lot of general things. But it does not know who the user is, what they did five minutes ago, what documents are relevant right now, or what rules apply to this specific situation. All of that has to be given to it – every single time. So the quality of that handoff is everything. Furthermore, as models get used in longer, more complex tasks, the handoff gets harder, and the cost of getting it wrong gets higher. That is why context engineering in AI has moved from a nice-to-have skill to a core engineering discipline.
Context Engineering in AI – What It Actually Means
Let us be clear about the definition, because the term gets used loosely. Context engineering in AI is the practice of designing, selecting, and structuring the information that gets passed to a language model – so the model has exactly what it needs to do its job well.
This is different from prompt engineering, which mostly means writing better instructions. Context engineering in AI goes a level deeper. It covers the full information environment the model operates in – the user’s history, the relevant documents, the current task state, the memory of past actions, and the rules that should govern this specific interaction. Moreover, it involves deciding what to leave out just as much as what to include, because a bloated context can confuse a model just as much as an empty one.
What Context Engineering Actually Involves
In practice, context engineering in AI covers several distinct layers of work:
- Memory design – What information from past interactions gets stored and recalled.
- Document retrieval – Which pieces of information get pulled in from external sources?
- Prompt construction – How instructions, examples, and constraints get structured.
- State management – Tracking what has happened so far in a multi-step task.
- Tool output handling – How results from external tools get formatted and passed back.
So context engineering in AI is really a discipline that spans architecture, data design, and product thinking all at once. It is not one skill. It is a set of connected decisions that all affect what the model actually sees when it generates a response.
AI Context Layers – The Architecture That Makes It Work
To understand why this matters so much, it helps to think in layers. A modern AI system does not just send a user’s message to a model and wait. There are several AI context layers sitting between the user and the raw model – each one adding or filtering information before the model ever sees a prompt.
These AI context layers are what allow a product to feel smart and personalised, even when the underlying model is the same one every other product uses. The difference is not the model. The difference is how those layers are built and maintained.
Types of Context Layers in Production Systems
Here is how AI context layers typically stack in a real production system:
- System layer – The foundation of the context stack. This layer contains the core instructions that define how the model should behave, including its role, tone of voice, objectives, constraints, and safety guidelines. It ensures responses remain consistent and aligned with the intended use case.
- Memory layer – Contains information about interactions from the past, including user preferences, tasks or contexts encountered in the past. Using this information, the model can give personalised and accurate answers without having users restate the same information.
- Retrieval layer – Brings in relevant documents, information from the knowledge base or from elsewhere in real-time. Rather than using only training data, the model can obtain accurate and up-to-date information that would help it answer the queries.
- Tool layer – Gives the ability to interface the model with external tools such as APIs, databases, searches or any other third-party tool that could be used to do anything such as fetching live data or making calculations.
- Session layer – Maintains the ongoing state of the current interaction, including user inputs, model outputs, and task progress. It helps the model stay aware of the conversation flow and immediate context.
Each layer adds information that the model would not have on its own. Furthermore, each layer also adds complexity – which is why context engineering in AI requires careful design to avoid passing the model too much, too little, or the wrong information entirely.
LLM context window optimisation – Why It Matters So Much
Every large language model has a context window – a limit on how much text it can process at once. LLM context window optimisation is the practice of making the best possible use of that window, because what you fit in it directly determines what the model can work with.
Poor LLM context window optimisation leads to predictable problems. Important information can get lost when the system runs for a long time. Older parts of the conversation may disappear before they are needed again. At the same time, less important details can take up space and push out key rules or instructions. So the model loses track of things it should have known.
Good LLM context window optimisation fixes all of this by being selective. Rather than dumping everything into the window and hoping for the best, it prioritises the most relevant pieces, compresses older information into summaries, and uses retrieval tools to pull in specific facts on demand. Consequently, the model stays focused, and the output quality holds up even as tasks get longer and more complex.
Limitations of Foundation Models in AI – The Honest Breakdown
It is worth being direct about the limitations of foundation models in AI, because understanding them is what makes the case for context engineering so clear.
Here is what foundation models cannot do on their own:
- Remember past conversations – Models have no persistent memory without external systems.
- Know current events – Training data has a cutoff, so real-time knowledge needs retrieval.
- Follow organisation-specific rules – Company policies have to be given in the context.
- Stay personalised – User preferences need to be stored and retrieved externally.
- Track multi-step task progress – The model cannot inherently know what step it is on.
So the limitations of foundation models in AI are not really about intelligence. They are about information access. Moreover, this is exactly why context engineering in AI exists – to solve these gaps at the system level rather than pretending the model does not have them.
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Conclusion
At Working Not Working, we believe the best people deserve the best information. The shift toward context engineering in AI is one of the biggest moves happening in the AI industry right now. Models are becoming commodities. The skill that sets teams apart is knowing how to build the layers around them. Learn it. Apply it. Connect with the opportunities that reward it – through a platform built for people who take their craft seriously.
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Frequently Asked Questions
Q1. What is context engineering in AI, and how is it different from prompt engineering?
Context engineering in AI is the design of the full information environment a language model works within – including memory, retrieved documents, tool outputs, and session state. Prompt engineering focuses mainly on writing better instructions. Context engineering in AI goes deeper, covering all the information that surrounds and shapes each model call.
Q2. Why are AI context layers so important in production systems?
AI context layers are what allow an AI product to feel personalised and smart, even when the underlying model is a standard one. Each layer – system instructions, memory, retrieval, tools, and session state – adds information the model cannot have on its own. Without these layers, a model is essentially blind to everything outside its training data.
Q3. What is LLM context window optimisation and why does it matter?
LLM context window optimisation is the practice of making the best possible use of a model’s context window limit. Poor optimisation leads to relevant information being cut off or crowded out. Good optimisation uses selective loading, summarisation, and retrieval to keep the most important information in front of the model at all times.
Q4. What are the main limitations of foundation models in AI?
The key limitations of foundation models in AI include no persistent memory, no real-time knowledge, no awareness of organisation-specific rules, no personalisation, and no inherent ability to track multi-step task progress. These are not intelligence gaps – they are information access gaps that context engineering in AI is specifically designed to solve.
Q5. How does context engineering in AI change what skills teams need?
As context engineering in AI becomes more central to building good AI products, the most valuable skills shift from model training to system design – specifically, designing the memory, retrieval, and data structures that feed models the right information. Teams now need people who understand both the technical and product-level implications of context design.
