Context engineering is the discipline of structuring, selecting, and delivering the right information to an LLM at the right time. Unlike prompt engineering — which focuses on how you phrase your instructions — context engineering focuses on what background knowledge accompanies those instructions. As AI systems take on more complex, multi-step tasks, context quality becomes the primary lever for output quality.
What context engineering is
Context engineering encompasses every decision about what information enters an LLM's context window: which documents to include, how to format them, in what order to present them, and how much detail to retain versus summarize. Where prompt engineering optimizes a single input, context engineering designs an information architecture that spans an entire workflow or product.
The term emerged as practitioners found that raw LLM capability was rarely the bottleneck. Models with access to accurate, well-structured context consistently outperform the same models given only a task description. A model that has read your project's architecture document before writing code will produce fewer off-spec features, fewer hallucinated APIs, and better test cases — not because it became smarter, but because it became better informed.
The four layers of context
Context arrives in four distinct layers. System context establishes persistent rules, persona, and capabilities — it's always present. Working context holds the current task: the specific instructions, files, or user input the model is responding to right now. Historical context carries prior turns in a conversation or prior outputs from a pipeline. Background context is the durable reference material the model consults: documentation, codebases, specifications, and planning documents.
Most context engineering work happens in the background layer. This is where structured artifacts — PRDs, architecture docs, user stories — earn their keep. The background layer is where tools like InitRepo operate: generating the structured planning documents that become a project's permanent context supply before a single line of code is written.
Persistent context: documents over prompts
The most reliable context engineering technique is durable documents. Instead of re-explaining your project in every prompt, write it down once — architecture decisions, data models, API contracts, user personas — and include the relevant sections with each request.
Durable documents have three advantages. First, they are cheap to reuse: written once, included across hundreds of prompts without extra work. Second, they are easy to audit: a wrong assumption in a document can be found and corrected once, fixing every downstream prompt. Third, they make handoffs tractable: a new agent session, a new team member, or a new model sees the same ground truth.
The discipline here is maintaining the documents alongside the code. When an architecture decision changes, update the spec. When a new API surface appears, add it to the reference. Context quality degrades exactly as fast as its underlying documents drift from reality.
Dynamic context and retrieval
Not all context can be included at once — context windows have token limits, and sending irrelevant material dilutes the signal. Retrieval-Augmented Generation (RAG) solves this by indexing a large document corpus and fetching only the sections relevant to a given query at inference time.
In a coding workflow, this might mean embedding your entire codebase and documentation, then pulling only the five most relevant files when asking the model to fix a specific bug. The model receives focused, high-signal context rather than a kitchen-sink dump. The challenge is retrieval quality: a RAG system that fetches the wrong sections can be worse than no retrieval at all, because it gives the model confident but incorrect grounding.
The P#S# coordinate system built into InitRepo's generated documents is designed with retrieval in mind. Every concept in the project plan receives a persistent coordinate that maps across all generated documents. When an agent needs context about a specific feature, the coordinate resolves to the exact relevant spans across the business analysis, PRD, architecture spec, and user stories simultaneously — one lookup, complete context.
Context for AI coding agents
AI coding agents — Cursor, Claude Code, Copilot, Cline — all benefit enormously from well-structured context. The typical failure mode is not capability; it's context poverty. An agent asked to implement a feature without seeing the data model, API design, or user story will make plausible-but-wrong choices that compound into tech debt.
The minimum viable context for a coding task includes: (1) the relevant user story or acceptance criteria, (2) the architecture decision that governs the area being changed, (3) the existing code in scope, and (4) any relevant constraints from the PRD. Providing all four in a structured format — distinct, labeled sections rather than a wall of text — gives the agent a fighting chance at producing first-pass-acceptable code.
See the context engineering for AI coding guide for tool-specific integration patterns, or the AI coding tool guides for how to feed planning documents into Cursor, Claude Code, Cline, and others.
Building a context-first workflow
Shifting to a context-first workflow means front-loading the work of writing structure. Before you write code, write the plan. Before you start a new agent conversation, prepare the relevant documents. Before a session ends, update your documents to reflect what changed.
In practice: start a project with a planning session that produces your architecture document and PRD. Keep them in version control alongside your code. When you start a new coding session, include the relevant planning document sections in your initial prompt. When the session produces new decisions, update the documents. The planning step is not overhead — it's amortized context investment that pays back across every subsequent prompt.
The payoff compounds over time. A project with a maintained context stack stays coherent as it grows; one without it accumulates prompt debt — the ever-growing effort to re-explain the project to each new session. Teams that invest in context engineering early ship faster, with fewer regressions, for longer.