How to Use Z.ai for Code generation sessions

Z.ai is an AI chatbot and agent platform powered by GLM models for building landing pages, games, 3D models, and other creative projects from prompts. Confirm live details on chat.z.ai before production use.

Practical Code generation sessions examples

Example 1

Scenario:
Z.ai — Code generation sessions (pass 1). Context: Z.ai is an AI chatbot and agent platform powered by GLM models for building landing pages, games, 3D models, and other creative projects fro

Objective:
Deliver a reviewable code generation sessions result using Z.ai.

Inputs:
- Verified facts from chat.z.ai
- Audience, channel, or technical constraints
- Success criteria and forbidden claims
- Relevant product surfaces: Chat, Agents, Image Generation

Workflow:
Open Z.ai → Configure for code generation sessions → Pilot with sample inputs → Review against success criteria → Iterate one axis → Finalize

Requirements:
- Use only verified Z.ai capabilities; do not invent features.
- Confirm live details on chat.z.ai before promising volume or pricing.
- Human-review before external publish, send, billing, or compliance use.

Expected output:
A concrete code generation sessions artifact plus a short verification checklist.

Example 2

Scenario:
Z.ai — Code generation sessions (pass 2). Context: Z.ai is an AI chatbot and agent platform powered by GLM models for building landing pages, games, 3D models, and other creative projects fro

Objective:
Deliver a reviewable code generation sessions result using Z.ai.

Inputs:
- Verified facts from chat.z.ai
- Audience, channel, or technical constraints
- Success criteria and forbidden claims
- Relevant product surfaces: Chat, Agents, Image Generation

Workflow:
Open Z.ai → Configure for code generation sessions → Pilot with sample inputs → Review against success criteria → Iterate one axis → Finalize

Requirements:
- Use only verified Z.ai capabilities; do not invent features.
- Confirm live details on chat.z.ai before promising volume or pricing.
- Human-review before external publish, send, billing, or compliance use.

Expected output:
A concrete code generation sessions artifact plus a short verification checklist.

Example 3

Scenario:
Z.ai — Code generation sessions (pass 3). Context: Z.ai is an AI chatbot and agent platform powered by GLM models for building landing pages, games, 3D models, and other creative projects fro

Objective:
Deliver a reviewable code generation sessions result using Z.ai.

Inputs:
- Verified facts from chat.z.ai
- Audience, channel, or technical constraints
- Success criteria and forbidden claims
- Relevant product surfaces: Chat, Agents, Image Generation

Workflow:
Open Z.ai → Configure for code generation sessions → Pilot with sample inputs → Review against success criteria → Iterate one axis → Finalize

Requirements:
- Use only verified Z.ai capabilities; do not invent features.
- Confirm live details on chat.z.ai before promising volume or pricing.
- Human-review before external publish, send, billing, or compliance use.

Expected output:
A concrete code generation sessions artifact plus a short verification checklist.

Example 4

Scenario:
Z.ai — Code generation sessions (pass 4). Context: Z.ai is an AI chatbot and agent platform powered by GLM models for building landing pages, games, 3D models, and other creative projects fro

Objective:
Deliver a reviewable code generation sessions result using Z.ai.

Inputs:
- Verified facts from chat.z.ai
- Audience, channel, or technical constraints
- Success criteria and forbidden claims
- Relevant product surfaces: Chat, Agents, Image Generation

Workflow:
Open Z.ai → Configure for code generation sessions → Pilot with sample inputs → Review against success criteria → Iterate one axis → Finalize

Requirements:
- Use only verified Z.ai capabilities; do not invent features.
- Confirm live details on chat.z.ai before promising volume or pricing.
- Human-review before external publish, send, billing, or compliance use.

Expected output:
A concrete code generation sessions artifact plus a short verification checklist.

Example 5

Scenario:
Z.ai — Code generation sessions (pass 5). Context: Z.ai is an AI chatbot and agent platform powered by GLM models for building landing pages, games, 3D models, and other creative projects fro

Objective:
Deliver a reviewable code generation sessions result using Z.ai.

Inputs:
- Verified facts from chat.z.ai
- Audience, channel, or technical constraints
- Success criteria and forbidden claims
- Relevant product surfaces: Chat, Agents, Image Generation

Workflow:
Open Z.ai → Configure for code generation sessions → Pilot with sample inputs → Review against success criteria → Iterate one axis → Finalize

Requirements:
- Use only verified Z.ai capabilities; do not invent features.
- Confirm live details on chat.z.ai before promising volume or pricing.
- Human-review before external publish, send, billing, or compliance use.

Expected output:
A concrete code generation sessions artifact plus a short verification checklist.

Checklist before you ship

• Confirm the workflow stays inside verified Z.ai capabilities
• Review outputs against chat.z.ai when accuracy or pricing claims matter
• Keep a short verification list for any claim you would publish externally