Prompt and Context Design for Claude Code Workflows

The most common reason a dynamic workflow underperforms is not a weak model or a broken tool — it is a context window stuffed with the wrong things and starved of the right ones. In Claude Code, the context you assemble is the program the model runs. Get it right and the agent feels like it understands your project; get it wrong and even a million-token window produces vague, distracted, expensive runs. This article is about the discipline of deciding what goes in and what stays out, and why those decisions move outcomes more than prompt wording does.

Context engineering is the practice of deliberately curating what information enters a model's context window so the model has exactly what it needs to act well and nothing that distracts it. The discipline matters precisely because more is not better: every token you add competes for the model's attention and your budget, so the goal is the smallest context that fully supports the task.

Why more context hurts

There is a seductive intuition that a bigger window means you should fill it — paste the whole codebase, every doc, the full API reference. In practice that degrades performance. Irrelevant content dilutes attention, makes it harder for the model to find the load-bearing facts, and increases the chance it latches onto something tangential. A large window is a budget to spend carefully, not a bucket to fill.

The sharper framing is signal-to-noise. Each piece of context either raises the probability of a good next decision or lowers it, and plenty of plausibly-relevant material lowers it by crowding out what matters. The skill is ruthless curation: include what changes the answer, exclude what merely could be related. A lean, high-signal context beats a comprehensive one almost every time.

The four things worth their tokens

Some context reliably earns its place. First, the goal stated with its boundaries and done condition — the model cannot plan well toward a target it cannot see clearly. Second, standing project facts: the stack, the commands, the conventions that are always true and that the model would otherwise waste turns rediscovering. Third, the specific code, data, or documents the current task actually touches. Fourth, the recent transcript, so the model knows what it has already tried.

flowchart TD
  A["Candidate information"] --> B{"Changes the next decision?"}
  B -->|No| C["Leave it out"]
  B -->|Yes| D{"Always true for the project?"}
  D -->|Yes| E["Persistent instructions"]
  D -->|No| F{"Procedure for a task type?"}
  F -->|Yes| G["Skill, loaded on demand"]
  F -->|No| H["Inline in this run only"]

The decision tree above is the whole method. Start by asking whether a piece of information would actually change what the model does next; if not, it does not belong in context at all. If it would, route it to the layer that matches its lifespan — persistent instructions for always-true facts, on-demand skills for task-type procedures, and inline context for this-run-only material.

What to leave out, and why

Leave out anything the model can fetch when it needs it. With tools and skills available, you do not pre-load the entire API reference — you let the model pull the one endpoint it needs, when it needs it. Progressive disclosure beats pre-loading: keep short pointers in context and load the full content on demand, so the window stays lean until depth is actually required.

Also leave out stale and redundant material. Tool output from twenty turns ago that has already been acted on is noise now; a verbose log when a one-line summary suffices is noise; the same fact repeated across three layers is noise that can also contradict itself after an edit. Part of good context design is active pruning — summarizing and dropping what has served its purpose so the live window reflects the current state, not the whole history.

Layering by lifespan, in practice

The persistent layer (CLAUDE.md and project configuration) is loaded every single turn, which makes it the most expensive real estate you own — so it holds only durable, high-value facts, tightly written. Skills are the on-demand layer: a one-line description sits in context cheaply, and the full procedure loads only when the task makes it relevant. The transcript is the run layer, carrying this session's decisions and getting compacted as it grows.

Matching content to layer is most of the craft. A standing convention dropped into a single chat message will get compacted away on a long run and silently stop being honored; a long task-specific procedure jammed into CLAUDE.md taxes every unrelated call. Put always-true facts in the persistent layer, how-to procedures in skills, and run-specific state in the transcript, and the model gets the right information at the right cost on every turn.

Designing the prompt itself

With context layered well, the prompt's job narrows: state the goal, the boundaries, and the done condition clearly, and ask for reasoning before consequential action. You do not need elaborate role-play or verbose instructions when the standing context already carries the project's facts and conventions. The best workflow prompts are short and specific because the heavy lifting lives in the durable layers, not in the message you type.

One habit pays off repeatedly: ask the model to surface its plan or diagnosis before it acts. This gives you a checkpoint, gives the model a chance to catch its own mistake, and produces a transcript that tells you whether a failure was bad reasoning or bad execution. It is a tiny addition to the prompt with outsized returns on both reliability and debuggability.

Handling context as the run grows

Context design is not a one-time setup; it has to hold up over a long-running workflow whose transcript keeps expanding. As the run accumulates tool output and intermediate reasoning, the assembler compacts older material to make room, which means anything you needed to stay verbatim must live somewhere durable rather than in a single early message. Plan for compaction from the start: durable facts in persistent instructions, procedures in skills, and a willingness to let the transcript shed detail it no longer needs.

This is also where subagents earn their place in context terms. A subtask that would otherwise dump a large volume of intermediate text into the main window can run in its own context and hand back only a compact conclusion, keeping the orchestrator's window focused on the goal. The principle is the same one that governs the whole discipline — protect the high-signal context by routing low-signal, high-volume work somewhere it will not crowd out what the model actually needs to keep deciding well.

Closing the loop with evals

Context design is empirical, not theoretical — you find out what belongs by running the workflow and reading the transcripts. Keep the runs, especially the bad ones, and turn them into a small eval set. When a run fails because a fact was missing, add it to the right layer and confirm the failure no longer reproduces. When a run wandered because of noise, prune it and verify the improvement. Over time this turns context design from guesswork into a measurable practice that compounds.

The compounding is the point. Each fix to your context layers is permanent in a way a one-off prompt tweak is not: a fact added to CLAUDE.md helps every future run, a sharpened skill description helps every task that loads it, and a pruned source stops costing you on every turn forever. Teams that treat context as a living artifact — curated, versioned, and tested against recorded runs — find that their workflows quietly get sharper and cheaper month over month, while teams that re-improvise context per task stay stuck at the quality of their last lucky prompt. Context engineering is the discipline that makes dynamic workflows dependable, and it is mostly the unglamorous work of deciding, again and again, what truly earns its place in the window.

Frequently asked questions

If the window is huge, why not just include everything?

Because irrelevant context lowers the quality of the model's decisions by diluting attention and burying the facts that matter, and it raises cost on every turn. A large window is a budget to spend on high-signal content, not a reason to stop curating. Lean, relevant context outperforms comprehensive context.

How do I decide whether something goes in CLAUDE.md or a skill?

Ask how long the fact stays true. Always-true project facts belong in persistent instructions that load every turn, so keep them short. Procedures for a specific kind of task belong in skills that load only when relevant, so they can be richer. Lifespan determines layer.

What is the cheapest way to improve a workflow's context?

Read the transcript of a run that went wrong. It shows exactly which fact was missing or which noise distracted the model, so you can add or prune one thing and verify the fix. Empirical iteration on real transcripts beats theorizing about the perfect prompt.

Bringing agentic AI to your phone lines

CallSphere applies the same context discipline to voice and chat — agents that carry just enough context to resolve a call, pull details only when needed, and stay focused turn after turn. Hear well-engineered context on a live conversation at callsphere.ai.

Source & attribution: This is an independent, original explainer inspired by Anthropic's coverage on the Claude blog. Claude, Claude Code, Claude Cowork, Claude Opus, and the Model Context Protocol are products and trademarks of Anthropic. CallSphere is not affiliated with or endorsed by Anthropic.