The discipline of curating everything a model sees at inference — the token budget, system prompts, retrieval, memory and history, tools and structured data, compaction and long context, and assembling it into a context pipeline.
Prompt engineering was about wording a single instruction well; context engineering is the broader discipline of deciding everything a model sees at inference time — and for agents, it is the discipline that matters most.
Prompt engineering was about wording one instruction well; context engineering is the broader discipline of deciding everything a model sees at inference — and for agents it is the skill that matters most.
Every token in the window costs money, adds latency, and competes for the model's attention, so the first skill of context engineering is treating context as a scarce budget to be spent deliberately.
Every token in the window costs money, adds latency, and competes for attention. The first skill of context engineering is treating context as a scarce budget to spend deliberately.
The system prompt is the one piece of context present on every single call, so how you structure its role, rules, and format is the highest-leverage writing in the whole system.
The system prompt is the one piece of context present on every call, so how you structure its role, rules, and format is the highest-leverage writing in the whole system.
Retrieval is how you put external, current knowledge into a model's window, and doing it well is a context-engineering problem — the goal is not the most documents, but the right ones.
Retrieval is how you put external, current knowledge into a model's window, and doing it well is a context-engineering problem — the goal is not the most documents, but the right ones.
A conversation that never forgets eventually overflows, so managing what history an agent carries forward — and how it remembers across sessions — is one of the defining problems of context engineering.
A conversation that never forgets eventually overflows, so managing what history an agent carries forward — and how it remembers across sessions — is a defining problem of context engineering.
Tool definitions and structured data quietly consume a large share of the context budget, and how you select, describe, and format them shapes both what fits and how well the model uses it.
Tool definitions and structured data quietly consume a large share of the context budget, and how you select, describe, and format them shapes both what fits and how well the model uses it.
When context threatens to overflow, you compress it; and when you have a huge window to spend, you still should not fill it — because a long context is not used as well as a short, focused one.
When context threatens to overflow you compress it; and when you have a huge window you still should not fill it — because a long context is not used as well as a short, focused one.
Everything in this series — the budget, the system prompt, retrieval, memory, tools, and compaction — comes together as a pipeline that assembles the right window on every single turn, deliberately rather than by accident.
The budget, system prompt, retrieval, memory, tools, and compaction come together as a pipeline that assembles the right window on every turn — deliberately rather than by accident.
This series is part of a larger body of work by Pratik Dhanave, an Agentic AI Architect writing about production AI systems, distributed systems, and cloud-native engineering. Explore all course series, browse every post, or find topics via the tag index.