For a couple of years, "prompt engineering" was the buzzword — the belief that the right magic words unlock a model's best answer. In 2026 that framing feels dated. Modern models are good enough that clever wording matters far less than what information you put in front of them. That job has a new name: context engineering.

Why Prompts Alone Aren't Enough

A perfectly worded prompt still fails if the model doesn't have the facts it needs. Ask it about your company's refund policy and, without that policy in context, it will confidently guess. The bottleneck isn't phrasing — it's the information the model can actually see at the moment it answers.

What Context Engineering Means

Context engineering is the discipline of assembling everything the model sees before it responds: the system instructions, the user's request, retrieved documents, past conversation, tool results, and the desired output format. Prompt engineering writes one of those pieces well; context engineering designs the whole payload.

The Context Window Is a Budget

Every model has a finite context window, and more is not automatically better. Fill it with irrelevant text and accuracy drops — models lose track of details buried in the middle of a long context. Treat the window like a budget and spend it only on what helps this specific answer.

• Put the most important instructions and facts near the top or bottom
• Trim retrieved chunks to what's relevant — don't dump whole documents
• Summarise long histories instead of pasting them verbatim

Retrieval, Memory and Tools

Three sources fill the context intelligently: retrieval (pull relevant documents with RAG), memory (recall earlier facts about the user or task), and tools (fetch live data on demand). Good context engineering decides which of these to use, and when, for each request — rather than cramming in everything.

Common Mistakes

Most context problems come from a few familiar habits. Watch for these before anything else.

• Stuffing everything "just in case" — noise crowds out the signal
• Forgetting to include the output format you actually want
• Never inspecting what the model really received before it answered

The Takeaway

If your LLM feature is underperforming, look at the context before you rewrite the prompt. Nine times out of ten the model is answering correctly — from the wrong or incomplete information you handed it. Get the context right and everything else gets easier. Want a second pair of eyes on your AI pipeline? Let's talk.