Getting Started with a Command-Line AI Coding Assistant: Installation, Configuration, and Your First Project

1. Understand the Configuration Chain

A command-line AI coding assistant typically consists of three parts:

• The local tool reads project files, generates modification plans, and executes commands.
• A remote model service performs the reasoning.
• An authentication token connects the local tool to the service.

When setup fails, the problem is often a mismatch between these components—for example, an invalid token, a misspelled model name, or an environment variable that is available only in one terminal session.

Troubleshoot the chain step by step: first confirm that the tool itself runs correctly, then verify that the token is valid, and finally check that the model name and service endpoint match the service provider’s documentation.

2. Prepare Your Environment

1. Runtime environment. Make sure your operating system and terminal version meet the tool’s requirements. Follow the official installation documentation, then use the tool’s version command to confirm that installation succeeded.
2. Account and token. Create an account with the service provider and generate an API token for local use. Most platforms display the complete token only once, so save it in a password manager immediately.
3. Billing method. Usage-based billing charges for the actual amount of input and output data consumed. Complex tasks often include more context, which increases usage. Refer to the provider’s pricing page for current rates; this article does not list specific prices.

3. Protect Your API Token

An API token is an account credential. If it is exposed, someone else may use your account quota. Follow these three principles:

• Do not put the token in source code, configuration templates, or version-control commits.
• Do not paste the complete token into chat applications, support tickets, or screenshots.
• Rotate the token regularly. If you suspect that it has been exposed, revoke the old token through the platform immediately and generate a new one.

4. Configure the Tool with Environment Variables

Environment variables are available only to the current process and its child processes. If you run export directly in a terminal, the variables will disappear when that terminal window is closed. For regular use, place the configuration in a user-level shell configuration file so that each new terminal loads it automatically.

Start with a temporary validation:

export API_TOKEN="<your-token>"
export MODEL_NAME="<model-name>"

These variable names are examples only. Use the names specified in the documentation for your tool.

After confirming that the temporary configuration works, make it persistent. Avoid placing the token directly in the main shell configuration file because such files may be synchronized to other devices or published through a personal configuration repository. A safer approach is to store the credentials in a separate file and restrict its permissions:

mkdir -p ~/.config/ai-assistant
cat > ~/.config/ai-assistant/env <<'EOF'
export API_TOKEN="<your-token>"
export MODEL_NAME="<model-name>"
EOF
chmod 600 ~/.config/ai-assistant/env
# Add the following line to the end of your shell configuration file
[ -f ~/.config/ai-assistant/env ] && . ~/.config/ai-assistant/env

chmod 600 allows only the current user to read and write the file. After opening a new terminal, use the following command to check whether the variable has been loaded without printing the token itself:

echo "${#API_TOKEN}"

A number greater than zero indicates that the variable is loaded. A result of zero usually means that the file path is incorrect or that the loading statement has not taken effect.

5. Choose a Model and Manage Costs

Different tasks require different levels of model capability. Select a model according to the complexity of the task:

Task type	Suggested model tier	Primary consideration
Cross-module architecture design and difficult bug diagnosis	Highest-capability tier	Prioritize reasoning depth; per-request cost is higher
Routine feature changes, comment updates, and small refactors	Balanced tier	Faster responses with moderate cost
Batch formatting and simple questions	Lightweight tier	Fastest responses for frequent use

You can control costs in three ways:

1. Set usage budgets and alert thresholds on the platform.
2. Limit the context scope so the assistant reads only directories related to the task.
3. Prevent the tool from repeatedly scanning large dependency directories, which can significantly increase input usage.

6. Service Groups and Third-Party Routing

Some platforms provide different service groups or routes with different pricing. Common differences include price, available models, stability, and data-handling practices. In many cases, lower-cost groups offer fewer models, making it difficult to maximize both price advantages and capabilities.

Use the following criteria when selecting a service group:

1. Model consistency. Send a simple request and inspect the actual response to confirm that the service is using the expected model.
2. Model coverage. Review the available model list and confirm that it includes the capability tier you need.
3. Service stability. Monitor request success rates and response latency over time instead of relying on a single test.
4. Data handling. Read the provider’s privacy policy and logging practices. When requests are routed through a third party, your code passes through that provider’s servers. For unpublished code or regulated data, evaluate the risks in advance and consider a solution that can be deployed privately when necessary.

7. Run Your First Project

Your first project should be small, include automated tests, and already be under version control. This makes every change easier to verify and roll back.

A recommended workflow is:

1. Go to the project root directory and start the tool.
2. Describe the task scope in one or two sentences. For example: “Add test cases for boundary inputs in the parsing module without changing the public interface.”
3. Review the modification plan generated by the tool. Allow it to write files only after you confirm the plan.
4. Run the project’s existing test command and make sure all tests pass.
5. Inspect the diff with your version-control tool. Review each file before committing the changes.

cd ~/projects/demo-service
# Start the assistant, enter the task description, then run the project tests
<project-test-command>

Beginners commonly make four mistakes:

• Giving the assistant an overly broad task that spans multiple modules.
• Accepting changes without reviewing the diff.
• Ignoring failed tests.
• Mistaking an exhausted usage quota for a tool malfunction.

8. Troubleshooting Common Problems

• Authentication failed: Check that the token is complete and does not contain extra spaces or line breaks.
• Model not found: Compare the model name with the provider’s available model list. Pay attention to capitalization and hyphens.