← Voltar aos guias

Better System Prompts for Coding Agents

Start with the job, not the model

The fastest way to improve a coding agent is to stop writing vague instructions like “help with code” and define the agent’s job in operational terms. A system prompt should tell the agent what it is allowed to do, what it should avoid, and how it should behave when it is uncertain. For example, if you want an agent to act like a senior engineer in an existing repo, say that directly: it should inspect the codebase first, preserve current patterns, and prefer small, reviewable changes over broad refactors.

In practice, I treat the system prompt like a mini operating manual. I include the target role, the boundaries, and the default decision rules. That gives the agent less room to improvise in the wrong direction.

Write the prompt around the workflow you want

Good system prompts for coding agents are process-oriented. They should guide the sequence of actions, not just the desired outcome. A strong prompt usually includes four parts:

• Context: What kind of repository is this, and what language or framework is in use?
• Behavior: Should the agent inspect files before editing, ask questions when specs are unclear, and keep changes minimal?
• Quality bar: Should it run tests, preserve formatting, and avoid unnecessary dependencies?
• Response style: Should it summarize what changed, list files touched, and call out risks?

A useful pattern is: “You are a coding agent working inside a production codebase. First inspect relevant files, then propose the smallest safe change, then implement it, then verify with tests or a reasoned check if tests are unavailable.” That kind of wording is specific enough to shape behavior without micromanaging every step.

Add guardrails for the most common failure modes

Most bad agent behavior comes from a few repeatable mistakes: hallucinating file contents, over-editing, skipping tests, or making architectural changes when a bug fix was requested. Your system prompt should preempt those failures. Tell the agent not to guess at unseen code. Tell it to stop and ask if a decision depends on missing context. Tell it to avoid package upgrades, large refactors, or style changes unless explicitly requested.

It also helps to define escalation rules. For example: “If the task affects authentication, billing, or data migration, explain the risk before editing.” Or: “If the change requires more than three files, provide a short plan first.” These small constraints reduce costly mistakes, especially in production repos.

Use a simple template and then test it like software

Think of the first version of your system prompt as testable code, not finished prose. A practical template might read: “You are a precise coding assistant for [stack]. Before changing code, inspect relevant files. Make the smallest correct change. Preserve existing conventions. If requirements are ambiguous, ask one focused question. After editing, summarize the files changed, the reasoning, and any follow-up checks.”

Then test it with three tasks: a tiny bug fix, a medium-sized feature, and an ambiguous request. Watch where the agent struggles. Does it over-explain? Skip verification? Touch too many files? Use those failures to revise the prompt. The best prompts are rarely written once; they are tuned over several real tasks.

Iterate cheaply so you can refine faster

Prompt testing should be fast and low-cost, because the first draft is never the best draft. This is where a relay like 59API is genuinely useful. It gives you pay-as-you-go access to Claude models and GPT models through the same base URL, https://api.59api.com, and it works with Claude Code, Codex, and any OpenAI SDK. Because it uses native official-quality models with no downgrade, you can evaluate prompts against realistic model behavior instead of a weakened substitute.

That matters when you are comparing versions of a system prompt. If you can run dozens of small tests cheaply, you can improve the prompt based on evidence instead of intuition. The low cost is especially helpful if you are validating behavior across different models or team environments, and the referral rebate makes experimentation even easier for repeat users.

Review the output format as carefully as the instructions

A strong coding agent prompt does not just control actions; it controls the final answer. Ask the agent to return a short summary, list modified files, note assumptions, and mention any checks it could not complete. That makes the output easier to review in code review, easier to paste into issue trackers, and easier to compare across prompt versions.

If you are building an internal workflow, this is often the difference between a helpful assistant and a noisy one. The agent can be smart, but if the response is inconsistent, the team loses time reading it.

Ship the prompt, then keep improving it

Once your prompt consistently produces small, accurate, reviewable changes, freeze that version and use it as the default. Keep a changelog for prompt edits the same way you would for code. Over time, you will notice that the best system prompts are not the longest ones. They are the clearest ones.

If you want to iterate on coding-agent prompts without burning budget, sign up for 59API and use it as your test harness for prompt experiments. Cheap, compatible access makes the feedback loop much faster.

Preços · Docs