Getting Started with Coding Plan: Environment and Workflow

Coding Plan is GLM's subscription plan for coding scenarios, built around the reasoning and coding capabilities of the GLM-5.3/5 series, wiring the model into the development environment you already use. This piece skips the concepts and focuses on getting it running: how to configure the environment, how to write your first prompt, and what rhythm to use for daily iteration. Plan details and quotas are subject to the official page.

1. Environment Setup: Three Things

Wiring up Coding Plan typically involves three steps: getting credentials, choosing an integration path, and verifying connectivity.

1. Credentials: After activating Coding Plan in the official console, obtain your API Key (see the official page for the exact entry point). Never commit the key to your repo—keep it in environment variables. 2. Integration: Two common routes—call the API directly via the BigModel platform; or plug GLM's base URL and model name into coding tools that support custom model endpoints (various CLI agents, IDE plugins). 3. Verify: Send a minimal request to confirm the pipeline works before building any workflow on top of it.

Environment variable example (Linux/macOS):

bash
export GLM_API_KEY="your-key"
export GLM_BASE_URL="https://open.bigmodel.cn/api/paas/v4"  # subject to official docs

2. Your First Prompt: Write Tasks as Specs

Many people's first prompt to a coding model is "write me a scraper." Open-ended tasks like that produce inconsistent output. A far more effective approach is writing the task as a spec: inputs, outputs, constraints, acceptance criteria. With thinking mode enabled on the GLM-5 series, structured specs get noticeably steadier reasoning.

A copy-ready template:

You are a senior backend engineer. Implement a function with the following requirements:

[Language & version] Python 3.11, standard library only
[Input] A list of strings, paths, each shaped like "dir/sub/file.txt"
[Output] A dict grouped by directory level: {dir: [filename, ...]}, files sorted lexicographically
[Constraints] Do not mutate the input; handle an empty list; reject any path containing ".." by raising ValueError
[Acceptance] Provide at least 5 test cases (including edge cases) before the implementation
[Output format] Test code and implementation in two separate code blocks, each preceded by a one-line note

Key points: constraints come before the task, and acceptance criteria force the model to think before writing code.

3. Daily Workflow: Small Steps + Follow-up Iterations

The biggest mistake in coding scenarios is dumping one huge task. Recommended rhythm:

• Split small: change one function or one module at a time; run tests immediately after each change.
• Follow up with the error: when a test fails, don't re-describe the task—paste the traceback and the relevant code back and ask the model to fix it in context.
• Say what not to touch: telling the model which files are off-limits sharply reduces out-of-scope edits.

Follow-up template:

In the previous round you modified src/parser.py, and 2 test cases in test_parser.py now fail.

Error output:
<paste full traceback>

Relevant code:
<paste code near the failing assertions>

Requirements:
1. Only modify src/parser.py; do not touch test files or other modules
2. Explain the root cause in one or two sentences, then give the complete fixed function
3. If you believe the tests themselves are wrong, explain why and suggest test changes—but do not modify tests by default

4. When to Turn On Thinking Mode

GLM's deep thinking mode fits three kinds of tasks: architecture decisions and option comparison, root-cause analysis of complex bugs, and planning changes across multiple files. Simple completions, formatting tweaks, and single-function implementations run faster and cheaper with thinking off. The litmus test: would a mid-level engineer need ten minutes of thought before doing this task? If yes, enable thinking.

For image-based scenarios (recreating UI from screenshots, parsing error screenshots), combine GLM-4V's visual understanding—feed the screenshot alongside your text description.

5. Quotas and Tool Choice

Coding Plan's quota rules, concurrency limits, and model tiers are subject to the official page. When picking an integration tool, watch three things: whether it supports a custom base URL (determines GLM compatibility), whether thinking mode can be toggled (keeps cost controllable), and whether it handles long context pasting smoothly (determines how well the follow-up iteration style works).

Coding Plan's real value isn't the model itself—it's embedding the GLM-5 series' coding capabilities into your daily workflow. Get the environment running first, then adopt the rhythm of "spec-style prompts + small-step iteration." After a couple of iteration cycles you'll naturally build your own prompt library—and when you do, come back to GLMMODEL to see how others are writing theirs.