𝗖𝗼𝗻𝘁𝗲𝘅𝘁 𝗘𝗻𝗴𝗶𝗻𝗲𝗲𝗿𝗶𝗻𝗴 𝗳𝗼𝗿 𝗣𝗿𝗼𝗺𝗽𝘁 𝗘𝗻𝗴𝗶𝗻𝗲𝗲𝗿𝘀
People say prompt engineering is dead. They say context engineering replaced it. This is wrong.
Prompt engineering is just one part of context engineering. Prompting is what you say. Context engineering is everything the model sees. This includes system prompts, tool definitions, retrieved documents, and memory.
Your prompt is a single piece of a larger puzzle. The surface area just got bigger.
Do not try to use massive context windows to solve everything. A million-token window does not mean a million tokens of useful attention. More input can actually make models perform worse.
You must watch out for two main issues:
• Lost in the middle: Models focus best on the start and end of a prompt. They struggle when important data sits in the middle. • Context rot: Reasoning and recall degrade as input length grows. Even simple tasks become harder for the model when the window is full.
The goal is to find the smallest set of high-signal tokens that get the job done. Treat your context window like a budget.
Use these six patterns to manage your budget:
• Attention budgeting: Cut all boilerplate and redundant text. Use only high-signal content. • Retrieval placement: Put relevant data at the start or the end. Avoid placing critical info in the middle. • Tool-result formatting: Return condensed data. Do not dump large API objects into the window. • Schema design: Use structured outputs. Keep nesting levels low to prevent errors. • System vs. turn separation: Keep core rules in the system prompt. Keep specific data in the user turns. • Memory and compaction: Save state outside the window. Summarize old data to keep the thread alive.
Context engineering is not just RAG. It is about placement and restraint. If you already prompt well, you are not starting over. You are learning to manage the budget your prompt spends.
Source: https://dev.to/anoopk/context-engineering-for-people-who-already-know-how-to-prompt-2pj1
Optional learning community: https://t.me/GyaanSetuAi