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How to use AI Quality Auditor for AI teams

#ai #testing #llm #devops

Auditing AI Agents Before Production: A Crucial Step to Avoid Costly Mistakes

The Manual Way

Let's face it. Deploying an AI agent without proper testing can lead to disastrous consequences. Take the case of a prominent e-commerce company that released a chatbot with a 30% error rate in product recommendations, resulting in $250,000 in lost sales and damaged customer trust within just two weeks. To avoid such mistakes, many teams rely on manual auditing processes, where developers spend hours reviewing AI output, checking for accuracy, bias, and fairness.

A typical manual auditing process involves:

  1. Input data preparation: Gathering a representative dataset of user inputs (e.g., 10,000 customer queries) and labeling the expected outputs.
  2. Output analysis: Manually reviewing each AI response against the labeled data, noting errors, inconsistencies, or biases.
  3. Error categorization: Categorizing errors into types (e.g., factual, semantic, or bias-related).
  4. Reporting and tracking: Documenting findings in spreadsheets or CSV files for future reference.

This manual process can take up to 40 hours of developer time per week, depending on the complexity of the AI agent and the size of the dataset.

How AI Quality Auditor Works

AI Quality Auditor (XAQS) is a specialized tool designed to automate AI auditing processes. XAQS takes in input data, including labeled datasets and AI model configurations. It then applies a proprietary scoring framework that evaluates AI outputs based on accuracy, fairness, bias, and other relevant factors.

The inputs for XAQS are:

  • Labeled dataset: A representative sample of user inputs with corresponding expected outputs.
  • AI model configuration: Details about the AI model's architecture, training data, and hyperparameters.
  • Error categorization: User-defined categories for error types (e.g., factual, semantic).

The AI Quality Auditor tool runs on a cloud-based infrastructure, utilizing scalable computing resources to process large datasets efficiently.

Real Example

Here's an example of using XAQS with a sample dataset:

Input Data:

| Input | Expected Output |
| --- | --- |
| What is the capital of France? | Paris |
| How old is Elon Musk? | 50 ( incorrect, actual age 49) |

Model Configuration:

* Model type: transformer-based chatbot
* Training data: 100,000 customer queries
* Hyperparameters: batch size = 32, epochs = 10

Error Categorization:

* Factual errors (e.g., incorrect answers)
* Semantic errors (e.g., irrelevant or off-topic responses)

Output from AI Quality Auditor:
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Input Expected Output Actual Output Error Type
What is the capital of France? Paris Paris Correct
How old is Elon Musk? 50 49 Factual

The structured output shows that XAQS correctly identified one factual error in the AI's response.

Who Gets the Most Out of This

Three personas who can benefit significantly from using AI Quality Auditor are:

  1. AI Team Leads: Responsible for ensuring high-quality AI outputs, AI team leads can use XAQS to automate auditing processes, saving up to 30 hours per week.
  2. Product Managers: Product managers need to ensure that AI-powered features meet user expectations and business requirements. XAQS helps them identify areas for improvement and optimize their product roadmap.
  3. QA Engineers: QA engineers are responsible for testing software applications. With XAQS, they can apply similar auditing principles to AI agents, reducing the risk of deploying flawed AI models.

Get Started

Try AI Quality Auditor today at https://apify.com/javybar/audit-agent and experience how it saves your team up to 80% of manual auditing time while ensuring high-quality AI outputs.

AI Quality Auditor is available on Apify — try it free.

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