AI Agents
Discover hallucinations using our custom mini-screening template
18 nodes
225
164
Automatic trigger
Workflow Description
A specialized template for detecting hallucinations in language model outputs using a custom screening chain, combining advanced text processing with local models to evaluate response accuracy and filter unreliable content.
How it works
- 1.Trigger the workflow manually or via external event to initiate screening
- 2.Process text and data through LLM chains to analyze model responses
- 3.Split results and apply screening criteria to identify potential hallucinations
- 4.Merge and filter results to retain only verified, reliable data
- 5.Publish reports to social platforms or store for further review
Use cases
- Verify quality of AI model responses before deployment to end users
- Monitor accuracy of data generated by LLM systems in operational workflows
- Screen auto-generated content for sensitive applications requiring high reliability
Requirements
- Local language model setup (Ollama) or compatible LLM API interface
- Access to X/Twitter if publishing results is required
- Sample data or responses for testing and evaluation
Service Value
Ideal as a smart automation service combining integrations and AI to produce ready-to-use results.
Apps Used
LLM Chain
Lm Chat Ollama
Twitter/X
Lm Ollama
Details
Trigger
Automatic trigger
Nodes
18
Apps
4
Views
225
Downloads
164
How to Use
- 1.Click "Download Template"
- 2.Open your n8n dashboard
- 3.Go to Workflows > Import from File
- 4.Select downloaded file and configure credentials
Nodes Used (18)
/
Code
Code
Split Out1
Split Out
Basic LLM Chain4
LLM Chain
Ollama Chat Model
Lm Chat Ollama
When clicking ‘Test workflow’
Manual Trigger
Edit Fields
Set
Merge
Merge
Filter
Filter
When Executed by Another Workflow
Execute Workflow Trigger
Aggregate
Aggregate
Merge1
Merge
Basic LLM Chain
LLM Chain
Ollama Model
Lm Ollama
Sticky Note
Sticky Note
Sticky Note1
Sticky Note
Sticky Note2
Sticky Note
Sticky Note3
Sticky Note
Sticky Note4
Sticky Note