99% of n8n Builders are Doing It Wrong

Key Concepts

AI Agent Evaluation, Groundtruth Data Set, AGI (Artificial General Intelligence), DPAL (Data Privacy Assessment Language), Regression Testing, LLM (Large Language Model) as a Judge, Elucination vs. Faithfulness, Answer Relevancy, Contextual Relevancy, GEVAL, Synthetic Test Data, RAG (Retrieval-Augmented Generation).

Evaluating AI Agents: The Importance of a Groundtruth Data Set

The video addresses the challenges of building reliable and accurate AI agents, particularly within the N8N environment. It highlights the common "whack-a-mole" problem where fixing one issue introduces another, often without clear metrics to gauge improvement. The core argument is that a proactive "evaluation mindset" is crucial, involving the creation of a groundtruth data set before any coding begins. This data set should encompass various user intents and scenarios the agent is expected to handle.

• AGI Limitation: The video emphasizes that current AI agents require specialization due to the lack of AGI. Defining the scope (what's in and out of bounds) is essential for reliable performance.
• Time Investment: While creating a groundtruth data set requires upfront effort, it ultimately saves time and improves the quality of the AI agent.
• Confidence in Changes: Hard metrics derived from the evaluation process provide confidence when making changes to the system.

DPAL: An Open-Source AI Evaluation Framework

The video introduces DPAL as a powerful open-source AI evaluation framework, offering an alternative to N8N's built-in evaluation system, which can be expensive (minimum $800/month for self-hosting or $60/month for N8N Cloud Pro plan). The presenter created a REST API wrapper for DPAL to make it accessible from N8N.

• DPAL Features: DPAL can perform unit tests on specific components or end-to-end blackbox testing. It leverages LLMs as judges to evaluate the quality of the AI agent's responses.
• Metrics: DPAL offers 40+ ready-to-use metrics covering RAG, agents, multi-turn chatbots, safety, and multimodal aspects.
• GEVAL: GEVAL is a custom metric within DPAL that uses chain-of-thought reasoning to evaluate responses based on specific criteria defined by the user.

Analogy: The Basketball Court

The video uses a basketball court analogy to illustrate key concepts in AI evaluation:

• Shots (Test Cases): Each shot represents a test case or question posed to the AI agent.
• Green Shots (Correct Answers): Successful test cases where the agent provides the correct answer.
• Red Shots (Incorrect Answers): Failed test cases where the agent's response is incorrect.
• Distance (Difficulty): The distance of the shot represents the difficulty of the question.
• Out of Bounds (Out of Scope): Questions that are outside the intended domain of the AI agent.
• Clustering: Avoiding clustering tests in one area of the "court" ensures comprehensive testing across all features.

Practical Implementation: From Google Sheets to Air Table

The video discusses different ways to structure evaluation data sets:

• Basic Level: A simple Google Sheet with questions and expected answers.
• Advanced Level (Air Table): An Air Table base for more automation, including daily test runs, execution logs, evaluation results, scores, and reasoning for failures.

The Air Table approach enables automated regression testing to detect performance degradation due to model updates or other changes.

Model Degradation and Continuous Evaluation

The video highlights the importance of continuous evaluation, even in production environments, due to model degradation.

• Model Updates: Model providers often swap out models behind the scenes, potentially impacting the performance of the AI agent.
• Regression Detection: Regular evaluations can detect these performance regressions and trigger necessary adjustments.
• Notion Case Study: Sarah Saxs from Notion was able to roll out a new family of entropic models within 24 hours of them going live due to having an extensive evaluation framework.

Generating Synthetic Test Cases

The video explores methods for creating test cases:

• Manual Creation: Manually creating a set of 20-30 questions that the agent should be able to answer.
• Synthetic Generation: Using an LLM to generate test cases from documents ingested into a knowledge base.

Synthetic test cases can be a good starting point but should be reviewed and refined to ensure they accurately reflect real-world user interactions.

DPAL API Wrapper and Deployment on Render

The video provides a step-by-step guide on deploying the DPAL API wrapper on Render:

1. Create a Render Account: Sign up for a free account at render.com.
2. Create a Web Service: Add a new web service and connect it to the GitHub repository containing the DPAL API wrapper.
3. Configure Environment Variables: Set the OPENAI_API_KEY and API_KEYS environment variables.
4. Deploy the Web Service: Deploy the web service and access the API documentation at /docs.
5. Test the API: Use a HTTP request node in N8N to ping the DPAL server with a test case.

Integrating DPAL with N8N Workflows

The video demonstrates how to integrate DPAL into N8N workflows for automated evaluation:

1. Define Test Cases: Store test cases in a Google Sheet or Air Table base.
2. Create a Test Run Workflow: A workflow that fetches test cases and triggers the workflow being evaluated.
3. Implement an Eval Trigger: Add an eval trigger to the workflow being evaluated to handle test runs.
4. Trigger DPAL Metrics: Use custom code to set up metrics compatible with the DPAL API and trigger calls to the DPAL server.
5. Update the Log: Update the execution log with the evaluation results.

Elucination vs. Faithfulness

The video explains the difference between elucination and faithfulness problems in RAG systems:

• Elucination: The AI agent generates information that is not supported by the retrieved context.
• Faithfulness: The AI agent trusts incorrect or incomplete information from the vector store.

DPAL's faithfulness metric evaluates whether the agent's output factually aligns with the retrieved context.

Custom Nodes and Metric Library

The presenter created a library of custom nodes in N8N that trigger various DPAL metrics, including:

• Custom Metrics (GEVAL): Content quality, etc.
• RAG Metrics: Faithfulness, contextual relevancy, retrieval.
• Safety Metrics: Bias, toxicity, role violation, PII leakage.
• Agentic Metrics: Tool correctness, argument correctness, task completion.
• Multi-Turn Chat Metrics: Role adherence, turn relevancy, knowledge retention.

Generating Synthetic Test Cases from Documents

The video demonstrates how to generate synthetic test cases from documents using a long context window LLM:

1. Upload Documents: Upload documents to a Google Drive folder.
2. Extract JSON: Extract the JSON data from the documents.
3. Send to LLM: Send the JSON data to an LLM with a system prompt that instructs it to generate test cases from the perspective of a customer.
4. Save Test Cases: Save the generated test cases to an Air Table base.

Conclusion

The video provides a comprehensive guide to evaluating AI agents using DPAL and N8N. It emphasizes the importance of a proactive evaluation mindset, the creation of a groundtruth data set, and continuous monitoring to ensure the reliability and accuracy of AI systems. The DPAL API wrapper and the step-by-step instructions for deployment and integration make it easier for developers to implement robust evaluation processes in their N8N workflows. The presenter encourages viewers to explore the DPAL wrapper on GitHub and join the community for access to the full N8N DPAL system.