From a Single Question to a Whole Context
A few years ago, the secret to good AI results was 'asking the right question' — prompt engineering. Today the picture is bigger. In modern AI systems — especially agents and RAG-based products — what decides the outcome is not a single prompt but the entire context the model sees at that moment. Designing that whole is called context engineering, and in 2026 it has moved to the center of AI engineering.
In this article we explain, in plain language, why context is a bigger matter than a single prompt, why the context window is a scarce resource, and the ways to assemble the right context.
From Prompt Engineering to Context Engineering
Prompt engineering is carefully writing the instruction you give the model: be clear, assign a role, add examples. It is still valuable; we covered it in detail in our prompt engineering article. But when an agent or a real product is involved, your instruction is not the only thing that reaches the model. System instructions, the user's past messages, retrieved documents, tool outputs, notes pulled from memory — all of them crowd into the same 'context window.' Context engineering is deciding what goes into that window, how much, and in what order.
The Context Window: A Scarce Resource
A model's context window is the limit of the text it can 'see' at once. Windows have grown, but they are still scarce: the more you stuff into the window, the more the model gets distracted, slows down, and costs rise. So context is not a bottomless bin but a budget to be managed carefully. A good system assembles the minimum-yet-sufficient context for each query; every unnecessary piece drags down both cost and accuracy.
Not More Context, but the Right Context
Counterintuitive but important: it is not true that the more context you give the model, the better the answer. Research shows that information in the middle of long contexts is often overlooked — the 'lost in the middle' effect. A window full of irrelevant documents pulls the model away from the right information. The real skill is not cramming in a lot, but selecting and foregrounding the right pieces needed right now. In context, quality matters more than volume.
Ways to Assemble Context
- Retrieval: Searching for and adding the most relevant pieces for the question rather than all the data — the heart of RAG.
- Summarization: Compressing a long history or documents to make room.
- Memory: Recalling persistent notes from earlier interactions when needed.
- Structure and order: Placing the instruction, sources, and question in the order the model will use best.
Context Engineering at İçtiHub
In law this is especially critical. A good answer to a legal question requires bringing together the relevant provisions, current case law, and the specifics of the situation correctly. But stuffing hundreds of irrelevant rulings into the window both raises cost and corrupts the answer. A large part of what we do at İçtiHub is context engineering: retrieving the right legal pieces for each query and presenting them to the model in the right order and amount. Semantic search and RAG are the tools; context engineering is the discipline of using them well.