Context Engineering
Context engineering is the discipline of deciding exactly what information enters a model's context window, in what order and at what cost.
/ quick answer
Prompt engineering writes instructions; context engineering manages the whole payload — system rules, retrieved knowledge, tool schemas, conversation history and tool output. The core moves are: retrieve instead of dumping, summarise before re-injecting, cache stable prefixes, and cut anything that hasn't changed a decision. Most agent quality and cost problems are context problems.
What is Context Engineering?
Prompt engineering writes instructions; context engineering manages the whole payload — system rules, retrieved knowledge, tool schemas, conversation history and tool output. The core moves are: retrieve instead of dumping, summarise before re-injecting, cache stable prefixes, and cut anything that hasn't changed a decision. Most agent quality and cost problems are context problems.
What is an example of Context Engineering?
Replacing a 30k-token document dump with 6 retrieved chunks improved answer accuracy and cut cost per call by 85%.
Why does Context Engineering matter for AI and automation?
Context engineering is the discipline of deciding exactly what information enters a model's context window, in what order and at what cost. It connects to the workflows, prompts and tool stacks linked on this page, so you can move from definition to execution without leaving Onexial.
/ continue exploring
Related concepts
The vocabulary this page depends on.
- →AI Software Engineering
AI software engineering is the practice of building software where agents write most of the code and humans own architecture, review and verification.
- →MCP Resources
MCP resources are addressable read-only context — files, records, docs — that a client can pull into the model instead of calling a tool.
- →MCP (Model Context Protocol)
Open protocol that lets LLMs connect to tools, data sources and apps through a standard interface.
- →Context Window
The maximum amount of text (in tokens) an LLM can consider in a single call.
Related workflows
Turn this into a repeatable process.
- →Cut Agent Costs by 60% Without Losing Quality
A measurable cost-reduction pass for any agent already in production.
- →Run a Coding Agent on a Real Codebase Safely
Give an agent write access without letting it wreck main.
- →How to Create a Website with AI
Go from idea to a live, custom-domain website in one afternoon using AI builders.
- →How to Build an AI Content System
A repeatable pipeline that turns one input into publish-ready content across every channel.
Related tool stacks
The tools that run it in production.
- →Coding Agent Stack
Run coding agents with executable feedback and reviewable diffs.
- →AI Observability Stack
Traces, cost, evals and quality drift for AI systems in production.
- →AI Research & Knowledge Stack
Default toolset for analysts, founders and creators doing deep research with AI.
Related prompts
Reusable prompts for this job.
- →Coding Agent Task Spec Prompt
Writes the task brief that keeps a coding agent inside scope.
Comparisons & alternatives
Pick between the options.
- →RAG vs Long Context Windows
Is RAG obsolete now that context windows are 1M+ tokens? Not quite — here's when each wins.
- →ChatGPT vs Claude
Two leading conversational AI assistants compared across reasoning, writing, coding, and pricing.
- →Lovable vs Bolt
Two AI app builders compared on speed, backend, deployment, and production readiness.
- →OpenAI API vs Anthropic API
Choosing between the two leading LLM API providers for production apps.