How to Use ClickUp With Coding LLMs Step by Step

ClickUp can work alongside coding large language models (LLMs) to help you plan, track, and improve AI-powered software development workflows from a single workspace.

This how-to guide walks you through setting up ClickUp to manage prompts, reviews, and deployments when you use coding LLMs like those described in the best LLMs for coding overview.

Why Combine ClickUp and Coding LLMs

Before you build your system, it helps to understand what you gain by pairing ClickUp with AI coding tools.

• Centralize all prompt ideas, test cases, and bug reports
• Standardize how your team requests and reviews LLM-generated code
• Track quality, speed, and risk across AI-assisted work
• Document decisions and best practices so future projects improve

Using ClickUp as the control center keeps your LLM usage transparent, measurable, and scalable.

Step 1: Create a ClickUp Space for AI Development

Start by creating a dedicated Space in ClickUp so LLM-driven work does not get lost in general project noise.

1. Open your ClickUp workspace and click + Space.

2. Name it something like AI Coding & LLMs.

3. Choose who can access it and set basic permissions.

4. Select views you want by default, such as List, Board, and Docs.

This Space becomes the home for every prompt, task, and experiment related to coding LLMs.

Step 2: Build ClickUp Lists for Key LLM Workflows

Inside your AI Space, use ClickUp Lists to represent the main workflows you run with coding LLMs.

Core ClickUp Lists for LLM Projects

• Prompt Library – reusable prompts for test generation, refactoring, and documentation
• LLM Feature Requests – new ideas for AI-assisted features or automations
• AI Code Tasks – tickets where an LLM will generate or refactor code
• Review & QA – issues that need human review, testing, or refactoring

Each List in ClickUp should mirror how your team already works with coding LLMs while making room for future experiments.

Step 3: Design ClickUp Task Templates for LLM Coding

Task templates help you request and evaluate LLM-generated code in a consistent, auditable way.

ClickUp Task Template for LLM Code Generation

Create a task template in ClickUp with sections like:

• Goal: What you want the LLM to build or change
• Context: Links to specs, repositories, and related tasks
• Constraints: Language, framework, style, and performance needs
• Prompt: The exact prompt sent to the coding LLM
• Output: Link to the generated code or diff
• Risks: Security, performance, or compliance notes

Save this structure as a reusable ClickUp template so every LLM-related ticket follows the same pattern.

ClickUp Template for LLM Bug Fix Requests

For bug-related code generation, add fields for:

• Steps to reproduce
• Expected vs actual behavior
• Logs, traces, or screenshots
• Test coverage required before closing

With consistent templates, ClickUp becomes the single source of truth for how and why each LLM-generated change entered your codebase.

Step 4: Configure ClickUp Statuses for LLM Work

Statuses in ClickUp show where each LLM-related task is in its lifecycle.

Suggested ClickUp Statuses

• Draft Prompt – idea defined but not yet sent to an LLM
• Sent to LLM – prompt has been run in the coding model
• Under Review – engineer is reading and testing generated code
• Changes Requested – prompt or output must be refined
• Ready to Merge – code passes tests and peer review
• Deployed – included in a release

You can customize these statuses per ClickUp List to match your existing engineering workflows.

Step 5: Use ClickUp Custom Fields to Track LLM Details

Custom Fields in ClickUp help you capture specific information about each coding LLM interaction.

Useful ClickUp Custom Fields for LLMs

• LLM Provider (dropdown): model or vendor used
• Prompt Version (number or text): prompt iteration used to get the final code
• Risk Level (dropdown): Low, Medium, High
• Human Review Required (checkbox): whether manual review is mandatory
• Lines of Code Affected (number): scope of change
• Test Status (dropdown): Not Run, Failing, Passing

With these fields, ClickUp can surface dashboards and reports that show how LLM usage affects quality, velocity, and risk.

Step 6: Document LLM Practices With ClickUp Docs

Use ClickUp Docs to write and maintain your AI coding playbook.

What to Document in ClickUp Docs

• Approved coding LLMs and when to use each
• Security and privacy rules for prompts and code
• Prompt engineering best practices with real examples
• Review checklists for LLM-generated code
• Incident response steps if AI-generated code introduces issues

Link these Docs directly from relevant ClickUp Lists and tasks so contributors can quickly reference the latest guidance.

Step 7: Build ClickUp Views and Dashboards

Different roles need different visibility into LLM-driven work. ClickUp offers multiple views and dashboards to support this.

Helpful ClickUp Views

• Board View: See prompts and tasks move across LLM statuses
• List View: Sort tasks by LLM provider, risk level, or test status
• Calendar View: Track deployment dates and review deadlines
• Table View: Compare performance across models or teams

ClickUp Dashboards for LLM Performance

Create dashboards that show:

• Number of tasks involving LLMs per sprint
• Average review time for AI-generated code
• Bug rate for LLM vs non-LLM tasks
• High-risk tasks awaiting review

These insights help you decide where coding LLMs save time and where extra controls are needed.

Step 8: Integrate ClickUp With Your Dev Tooling

To fully operationalize AI coding, connect ClickUp with your repositories and CI tools.

• Link pull requests and commits to ClickUp tasks so reviewers see context and prompts.
• Use automations to change ClickUp statuses when PRs open, merge, or fail checks.
• Add links to your LLM playground or internal tools directly in task descriptions.

With these connections, ClickUp acts as the hub for both human and AI contributions.

Step 9: Review and Improve Your ClickUp LLM Setup

Your first setup is not final. Use ClickUp itself to track improvements to your LLM workflows.

• Create recurring tasks to review prompt templates and statuses.
• Log incidents where LLM-generated code caused issues and capture lessons learned.
• Refresh Docs and task templates as your coding LLM stack evolves.

Over time, your ClickUp workspace becomes a refined system for safe, efficient AI-assisted development.

Next Steps

If you want expert help designing scalable AI workflows, you can explore consulting options from partners like Consultevo, then tailor those recommendations inside your ClickUp setup.

To choose the right coding models to plug into your workflows, review the latest options in the official guide to the best LLMs for coding, then mirror your choices and policies inside ClickUp so every project follows the same reliable process.

Need Help With ClickUp?

If you want expert help building, automating, or scaling your ClickUp workspace, work with ConsultEvo — trusted ClickUp Solution Partners.

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