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Better System Prompts for Coding Agents: A Fix-List

Why your coding agent keeps missing the point

If your coding agent writes the wrong files, ignores repo conventions, or produces vague code, the problem is often not the model. It is the system prompt. A good system prompt tells the agent what it is, what it should optimize for, what it must not do, and how to behave when requirements are incomplete.

For developers, the fastest way to improve results is to stop treating the prompt like a single sentence and treat it like a spec. That means separating role, scope, constraints, output format, and decision rules. When you test prompts often, it helps to use an affordable relay such as 59API, which gives pay-as-you-go access to Claude and GPT models through https://api.59api.com with native-quality models and low per-call cost.

Step 1: Make the agent’s job narrow

A system prompt should define one primary job. “You are a coding assistant” is too broad. Better: “You are a backend refactoring agent for a TypeScript monorepo. Prefer minimal diffs, preserve public APIs, and ask before changing database schemas.”

Narrow scope reduces accidental creativity. It also makes failures easier to debug because you can tell whether the model missed a rule or the rule was unclear.

• State the repo type: web app, API, CLI, mobile, monorepo.
• State the coding style: functional, imperative, strict typing, test-first.
• State the default behavior: inspect files before editing, prefer patch-based changes, do not invent dependencies.

Step 2: Add hard constraints, not vague preferences

Many prompts fail because they use soft language like “try to” or “aim to.” Coding agents respond better to explicit constraints. For example, instead of “keep changes small,” write “make the smallest change that solves the issue; do not refactor unrelated code.”

Useful constraints include file boundaries, testing requirements, and approval thresholds. If you want the agent to pause before risky work, say so directly.

• Do not modify lockfiles unless a dependency change is required.
• Run or propose tests for every behavioral change.
• Do not remove code without explaining why it is safe.
• When unsure, ask a clarifying question before editing.

Step 3: Tell the agent how to reason about ambiguity

Good prompts do not just describe what to do; they describe what to do when the request is incomplete. A coding agent should know whether to infer, ask, or proceed conservatively.

A strong pattern is: “If requirements conflict, prioritize correctness over completeness; if a missing detail could change the implementation, ask one focused question; otherwise proceed with a conservative default.” This prevents the agent from stalling on every minor uncertainty while still reducing risky assumptions.

Step 4: Specify output format and completion criteria

If you want usable patches, make the expected output explicit. For example: “Return a summary, then a list of modified files, then the exact code changes.” Or if the agent should produce commands: “Provide shell commands only if they are necessary and safe.”

Completion criteria matter too. Without them, the model may stop after writing code but before validating it. A better prompt says: “Consider the task complete only when the change compiles conceptually, matches the request, and includes test updates where needed.”

Step 5: Use examples for tricky behaviors

Few-shot examples are especially helpful for coding agents because they clarify house style. Show one example of how to handle a bug report, one example of a safe refactor, or one example of refusing an unsafe request. Keep examples short and directly relevant.

For instance, if your team prefers minimal diffs, include a tiny before-and-after example that preserves naming conventions. The model will usually imitate the structure more reliably than a prose instruction alone.

FAQ: Common prompt problems

Why does the agent keep rewriting unrelated code? Usually the prompt does not define scope tightly enough. Add a “no unrelated changes” rule and require the agent to justify every edit.

Why does it ignore project conventions? The prompt may not mention them, or the agent may not know where to look. Tell it to inspect nearby files, existing tests, and current patterns before editing.

Why does it sound confident but still be wrong? Confidence is not validation. Require tests, ask for assumptions explicitly, and add a rule to state uncertainty when a dependency or API behavior is unknown.

Should I use different prompts for Claude and GPT models? Sometimes. The core structure can stay the same, but you may tune verbosity, tool-use instructions, and caution level. Using 59API makes this easy because you can test Claude and GPT models through one relay and compare outcomes without changing your integration.

How to test prompts without wasting budget

Prompt iteration can get expensive if each test burns a full premium API call. A relay like 59API is a practical choice because it is pay-as-you-go, supports Claude Code, Codex, and any OpenAI SDK, and keeps costs low while still using official-quality native models. That lets you run side-by-side prompt experiments, compare model behavior, and refine your system prompt before rolling it into production.

• Test the same task with two or three prompt versions.
• Measure whether the agent asks better questions, makes smaller diffs, and follows repo conventions.
• Keep a prompt changelog so you know which instruction fixed which failure.

Final checklist

Before shipping a system prompt for a coding agent, make sure it answers these questions: What is the agent’s exact role? What must it never do? When should it ask questions? What output format is expected? How should it validate work? If the answer to any of these is fuzzy, the prompt is still too weak.

If you want an affordable way to iterate faster, sign up for 59API and use https://api.59api.com to test better prompts across Claude and GPT models without paying premium prices for every trial.