How to Use Giskard for Chat workflows

Secure AI agents with Giskard’s continuous AI red teaming. Detect vulnerabilities, improve LLM security, and safeguard your AI systems. Confirm live details on giskard.ai before production use.

Practical Chat workflows examples

Example 1

Scenario:
Giskard — Chat workflows (pass 1). Context: Secure AI agents with Giskard’s continuous AI red teaming. Detect vulnerabilities, improve LLM security, and safeguard your AI systems.

Objective:
Deliver a reviewable chat workflows result using Giskard.

Inputs:
- Verified facts from giskard.ai
- Audience, channel, or technical constraints
- Success criteria and forbidden claims
- Relevant product surfaces: Chat, Open Source, Agents

Workflow:
Open Giskard → Configure for chat workflows → Pilot with sample inputs → Review against success criteria → Iterate one axis → Finalize

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

Expected output:
A concrete chat workflows artifact plus a short verification checklist.

Example 2

Scenario:
Giskard — Chat workflows (pass 2). Context: Secure AI agents with Giskard’s continuous AI red teaming. Detect vulnerabilities, improve LLM security, and safeguard your AI systems.

Objective:
Deliver a reviewable chat workflows result using Giskard.

Inputs:
- Verified facts from giskard.ai
- Audience, channel, or technical constraints
- Success criteria and forbidden claims
- Relevant product surfaces: Chat, Open Source, Agents

Workflow:
Open Giskard → Configure for chat workflows → Pilot with sample inputs → Review against success criteria → Iterate one axis → Finalize

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

Expected output:
A concrete chat workflows artifact plus a short verification checklist.

Example 3

Scenario:
Giskard — Chat workflows (pass 3). Context: Secure AI agents with Giskard’s continuous AI red teaming. Detect vulnerabilities, improve LLM security, and safeguard your AI systems.

Objective:
Deliver a reviewable chat workflows result using Giskard.

Inputs:
- Verified facts from giskard.ai
- Audience, channel, or technical constraints
- Success criteria and forbidden claims
- Relevant product surfaces: Chat, Open Source, Agents

Workflow:
Open Giskard → Configure for chat workflows → Pilot with sample inputs → Review against success criteria → Iterate one axis → Finalize

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

Expected output:
A concrete chat workflows artifact plus a short verification checklist.

Example 4

Scenario:
Giskard — Chat workflows (pass 4). Context: Secure AI agents with Giskard’s continuous AI red teaming. Detect vulnerabilities, improve LLM security, and safeguard your AI systems.

Objective:
Deliver a reviewable chat workflows result using Giskard.

Inputs:
- Verified facts from giskard.ai
- Audience, channel, or technical constraints
- Success criteria and forbidden claims
- Relevant product surfaces: Chat, Open Source, Agents

Workflow:
Open Giskard → Configure for chat workflows → Pilot with sample inputs → Review against success criteria → Iterate one axis → Finalize

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

Expected output:
A concrete chat workflows artifact plus a short verification checklist.

Example 5

Scenario:
Giskard — Chat workflows (pass 5). Context: Secure AI agents with Giskard’s continuous AI red teaming. Detect vulnerabilities, improve LLM security, and safeguard your AI systems.

Objective:
Deliver a reviewable chat workflows result using Giskard.

Inputs:
- Verified facts from giskard.ai
- Audience, channel, or technical constraints
- Success criteria and forbidden claims
- Relevant product surfaces: Chat, Open Source, Agents

Workflow:
Open Giskard → Configure for chat workflows → Pilot with sample inputs → Review against success criteria → Iterate one axis → Finalize

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

Expected output:
A concrete chat workflows artifact plus a short verification checklist.

Checklist before you ship

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