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Vidisha Chirmulay
MarCom Executive
Vidisha, a Marcom Executive at Nitor Infotech, enjoys writing about technology. The realm of marketing communication intrigues her. In add... Read More

If you have had the chance to test a GenAI system, you know that it spells a unique set of challenges compared to traditional software.

GenAI systems, powered by ML and AI, are dynamic, data-driven, and often self-learning.

This contrasts with traditional software that operates based on predefined logic and rules.

The key distinction lies in the unpredictability and non-deterministic nature of GenAI outputs. This calls for a different approach to testing.

In this blog, let’s explore what product testing with GenAI is all about. Specifically, we’ll discuss:

Product testing with GenAI Nitor Infotech

Fig: Product testing with GenAI

Well, without further ado, let’s dive straight in!

Automating the Requirement Analysis Phase by Leveraging AI

The requirement analysis phase can be significantly automated using AI.

By feeding input requirements into an AI system, it can:

  • Analyze the data
  • Identify key components
  • Generate potential test scenarios

This process includes a comprehensive risk assessment. This ensures that all critical areas are covered.

The AI then produces a detailed report. Human analysts step in to review this report and confirm the completeness and accuracy of the requirements captured.

RAGAS, which stands for Retrieval-Augmented Generation Assessment, is a framework designed to evaluate the performance of Retrieval-Augmented Generation (RAG) applications.

These applications combine the retrieval of information from an external database with the generation of responses by a language model.

The RAGAS framework provides metrics and leverages large language models (LLMs) to assess the quality of the RAG pipeline on a component level.

One new innovative approach is the “Needle in a Haystack test’’. This test is designed to gauge the performance of RAG systems across various sizes of context.

LLM benchmarking tests are standardized evaluations used to assess the capabilities and performance of Large Language Models (LLMs). These tests typically involve a set of tasks or questions, a dataset, and a scoring system to measure various aspects such as reasoning, comprehension, and language understanding. The benchmarks provide a way to compare different LLMs and track their progress over time.

If you’d like to get acquainted with diffusion models: the brain behind multimodal LLMs, head over to our blog.

Also,if you want to understand the spectrum of an LLM from training to inference, you will find this blog compelling.

Now, let’s consider the challenges involved in GenAI testing.

Challenges in GenAI Testing

As you know, GenAI systems can sometimes produce hallucinations or outputs that are plausible but not grounded in reality. Addressing these requires understanding the role of Large Language Models (LLMs) like GPT-3. These are trained on vast datasets to predict text sequences.

Combining LLMs with Retrieval Augmented Generation (RAG) can help mitigate hallucinations. This is by providing additional context and information retrieval capabilities to the model.

You might be wondering what the key testing principles are. Read on to find out…

Key Testing Principles

Key Testing Principles Nitor Infotech

Fig: Key testing principles

1. Known Version Testing: The known version testing principle emphasizes the importance of testing specific, identified versions of a GenAI model. Since each version may behave differently due to updates or changes in the training data, it’s essential to test these versions to ensure they meet the expected performance and reliability standards. Known version testing allows for a controlled environment. In this environment, the model’s outcomes can be predicted and verified against known benchmarks.

2. Impossibility of Exhaustive Testing: GenAI models, particularly those based on ML, can produce an impressive array of outputs in response to a wide range of inputs. This variability makes it impossible to test every single input-output combination. The goal of exhaustive testing is unattainable. This is due to the explosion of possibilities and the continuous learning nature of these models!

3. Focusing on High-Risk Areas: Given the impracticality of exhaustive testing, the focus shifts to high-risk areas. These are parts of the GenAI system that, if they fail, could lead to the most significant consequences. By identifying these areas, testers can prioritize their efforts to ensure that the most critical functions of the GenAI system are thoroughly tested and validated.

4. Statistical Methods for Test Coverage: To cope with the impossibility of exhaustive testing, you must employ statistical methods to ensure that a representative sample of scenarios is tested. This approach includes:

  • Random Sampling: Selecting a random set of inputs to cover a broad range of scenarios.
  • Stratified Sampling: Dividing the input space into strata based on certain characteristics and sampling from each stratum to ensure coverage across different categories.
  • Monte Carlo Simulations (Repeated Random Sampling): Using random sampling to understand the behavior of the system under different conditions and to estimate probabilities of different outcomes.

These statistical methods help in creating a testing framework that, while not exhaustive, is sufficiently comprehensive to give confidence in the GenAI system’s performance and reliability.

They allow testers to think smartly and make informed decisions about the quality of the GenAI system and to identify areas that may require further testing or refinement.

Now, text-based GenAI testing is not a very simple process. Let’s discuss the complexities in it.

Complexities in Text-Based GenAI Testing

Here are the complexities involved in text-based GenAI testing:

1. Interaction of RAG Contextual Data, LLMs, Prompts, and Settings: Text-based Generative AI (GenAI) systems, such as chatbots or content generators, rely on a complex interplay of various components:

  • Retrieval Augmented Generation (RAG): This mechanism enhances the response quality by retrieving relevant information from a large dataset or knowledge base.
  • Large Language Models (LLMs): These are the core of GenAI systems, trained on extensive text corpora to predict and generate human-like text.
  • Prompts: User inputs or questions that initiate the GenAI’s response generation process.
  • Settings: Configuration parameters that dictate how the GenAI operates, including tone, verbosity, and content filters.

2. Drift Phenomenon Over Time: As GenAI systems interact with users and consume new information, they can ‘drift’ from their initial training. This drift can manifest as changes in the style, tone, or type of content generated. It’s a natural consequence of continuous learning and adaptation. But it can lead to inconsistencies with the expected output.

3. Managing Bias and Maintaining Model Fidelity: Bias in AI refers to the system developing skewed perspectives or preferences based on its training data. Ensuring that the GenAI remains unbiased and true to its design – its fidelity – is crucial for providing reliable and fair outputs. This requires:

  • Regular Audits: Periodic checks to identify and correct biases that may have crept into the system.
  • Diverse Training Data: Using a wide-ranging dataset to minimize the risk of developing biases.
  • User Feedback: Incorporating user reports on problematic outputs to refine the model.

We decided to explore the bias and misinformation in AI chatbots in a special blog, which you can read once you’re through reading this blog.

4. Careful Monitoring and Adjustment: To manage these complexities, continuous monitoring is essential. This involves:

  • Performance Metrics: Tracking various metrics to assess the GenAI’s performance and identify any drift.
  • Update Cycles: Implementing scheduled updates to the model to correct drift and bias issues.
  • Testing Protocols: Establishing robust testing protocols that can detect and address the nuances of text-based GenAI behavior.

In a nutshell, text-based GenAI testing is a multifaceted task that requires:

  • A deep understanding of AI components
  • Vigilant monitoring for drift and bias
  • A commitment to maintaining the model’s integrity.

It’s a dynamic process that calls for ongoing attention and refinement. This is to ensure that the GenAI system remains effective, accurate, and fair.

Now that you are conversant with the complexities involved in text-based GenAI testing, it’s time to look at regression testing and automation.

Regression Testing and Automation

Regular regression tests are essential to monitor for drift and ensure the GenAI system continues to perform as expected. Automation plays a key role here, as it allows for frequent and consistent testing.

However, handling varying answers from a GenAI system can be challenging. This is because the system may provide different outputs for the same input over time.

You might like to read this blog that we put together, dedicated to the what, the when, and the why of regression testing.

Well, it’s time to wrap up the ideas in this blog.

All in all, ensuring the completeness of product testing in GenAI systems requires the following three key things:

  • A nuanced understanding of AI behavior
  • Innovative testing strategies
  • A commitment to continuous monitoring and improvement

As GenAI continues to evolve on its exciting journey, so must our approaches to testing, always striving for the highest standards of quality and reliability.

Send us an email with your views on this blog. Also, visit us at Nitor Infotech to discover details about our GenAI prowess.

Discover even more details about our team’s multifaceted abilities in weaving magic with Generative AI.

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