The GenAI Testing Playbook: Best Practices for AI-Driven Testing & Deployment

2024 was a landmark year for Generative AI. What went wrong? The answer lies in a step too often overlooked—rigorous testing. It’s no surprise that Gartner predicts 30% of GenAI initiatives will be abandoned by 2025.

Building GenAI applications without proper testing is like launching a rocket without checking the fuel. It’s risky and, frankly, irresponsible. What happens when your AI produces misleading or compromises sensitive data?

These aren’t hypothetical scenarios; they were real issues that caused countless GenAI initiatives to fall short last year. Transitioning from a pilot to a production-ready solution is no easy feat. The stakes are high, and cutting corners can lead to underwhelming outcomes, damaged trust, and wasted investments. Yet, 2024 showed us that skipping the essential step of thorough testing is a gamble no organization can afford to take.

In this blog, we’ll unpack what went wrong for so many GenAI projects and, more importantly, how to ensure yours succeeds. We’ll dive into real-world scenarios, highlighting risks such as hallucinations, model inaccuracies, and security lapses. But we won’t stop there—we’ll also guide you through practical strategies to tackle these challenges.

AI application developers, if you’re ready to turn lessons from the past into a roadmap for success, keep reading. We’ll show you how to test, refine, and deploy GenAI applications that don’t just survive—they thrive.

Let’s start with the basics.

What is Generative AI? 

GenAI is a cutting-edge technology that uses advanced language models to create diverse outputs, such as text, code, and even use cases tailored to specific scenarios. Trained on vast datasets, GenAI can learn human languages, programming languages, art, and other intricate subjects. By identifying and understanding patterns, it can generate predictions, insights, and innovative solutions.

For example, in the banking sector, GenAI can streamline innovation by generating relevant use cases for chatbot development, making it a powerful tool for transforming customer interactions.

You may already be familiar with some leading GenAI models:

• ChatGPT by OpenAI: Renowned for its conversational capabilities, it produces human-like responses across a broad spectrum of topics.
• DALL-E by OpenAI: Excels at turning text prompts into creative and visually stunning images.
• Google Bard: Integrated with tools like Gmail and Google Lens, it delivers context-aware, high-quality text outputs.

GenAI continues to reshape industries, driving innovation and efficiency with its ability to adapt and excel in diverse applications.

GenAI Vs Traditional AI   

Traditional AI requires analysts to train the chatbot with huge data covering all scenarios. In this way, the chatbot remains restricted to the trained data alone for predictions.

Generative AI represents the next generation of artificial intelligence. Generative AI models are trained on datasets to learn underlying patterns, such as structures and relationships within the data, enabling them to generate new content that resembles the training set. Essentially, it takes a step further by creating entirely new content on its own.

GenAI for Chatbots

Traditional chatbots are typically built using predefined rules or programming logic. In some organizations, they are designed using nodes such as entity nodes and service nodes, which mimic the use case workflow. This approach makes it more accessible for users without a programming background.

Resource Requirements
Building a traditional chatbot often involves a team of resources, including business analysts to identify use cases, developers to create the bot, NLP analysts to fine-tune language understanding, and QA specialists to ensure quality and accuracy.

Explicit Training
Once the chatbot flow is created, the model undergoes explicit training using domain-specific test data to ensure it delivers accurate responses to user input.

Example Use Case
In the IT support domain, a traditional chatbot can automate an IT ticketing system, efficiently routing and prioritizing tickets to streamline workflows.

GenAI Node

Kore.ai offers a powerful “GenAI Node” feature, enabling organizations to fully leverage the capabilities of large language models (LLMs) and Generative AI to create dynamic, human-like conversational experiences. This innovative tool allows you to define specific entities for collection and establish business rules to govern their usage. The XO Platform intelligently orchestrates these conversations, leveraging contextual intelligence to ensure alignment with your enterprise’s unique business rules.

Additionally, you can set precise exit rules to seamlessly transition conversations to a virtual assistant or a human agent when needed, ensuring a smooth and efficient customer experience.

Learn more on how you can use GenAI Node in your applications.

Kore.ai also provides flexibility to integrate with custom LLM. 

With the XO Platform’s plug-and-play integration with premium AI services, you can quickly unlock the potential of LLMs. Alongside pre-configured or default prompts, you can create custom prompts tailored to specific use cases, ensuring optimal performance for your unique requirements. 

Benefits of GenAI

On the other hand, GenAI chatbot development is considered more streamlined than traditional AI building for several reasons.

Pre-trained language models, such as BERT and RoBERTa, enable the elimination of the need for training from scratch.

Personalization: Personalization is crucial for keeping clients engaged, and GenAI excels at delivering tailored responses.

The simplified architecture and reduced requirement for domain-specific knowledge contribute to faster development and deployment.

GenAI can expedite development by quickly generating use cases, code, and expected output data based on functionality descriptions and user inputs.

Additionally, GenAI chatbots can be trained on smaller datasets, making them more scalable and requiring less maintenance, resulting in a more efficient and cost-effective solution.
In scenarios where the product undergoes frequent requirement changes, GenAI adapts by automatically updating test scripts, mitigating the challenge of script maintenance.

Continuous Learning: GenAI's ability to learn continuously improves test coverage, boosting QA efficiency.

Overall, these advantages allow GenAI chatbot development to be completed in a matter of weeks or months, at a lower cost, with reduced development time and resources compared to traditional AI systems. For example, in the IT domain, a GenAI chatbot can create a knowledge base that answers common IT-related questions.

Kore.ai offers a hybrid approach, where certain use cases within a domain can utilize the traditional AI approach (using nodes), while others can leverage the GenAI approach using the GenAI node.

Click here for more information.

GenAI Deployment & AI-Driven Testing - How to Begin?

Should the GenAI approach be used for all scenarios?
The answer is “No.”

There tends to be a misconception that GenAI should be applied to all scenarios, but this may not always be necessary. The decision should be guided by task requirements, available resources, and desired outcomes. In some cases, a hybrid approach leveraging both GenAI and traditional AI might be the most effective solution.

Use Cases for Traditional AI
Predictive analytics, anomaly detection, or classification tasks where clear rules and patterns exist are scenarios where traditional AI can be utilized effectively.

Example 1: Disease Risk Prediction (Medical Domain)
In healthcare, a traditional AI model is used to predict the risk of diseases such as diabetes or heart conditions. The model analyzes structured patient data, including age, weight, medical history, blood pressure, and cholesterol levels, providing doctors with valuable insights for early intervention.

Example 2: Employee Performance Evaluation (HR Domain)
In HR systems, traditional AI analyzes employee performance data, such as KPIs, feedback scores, attendance, and project completion rates.

Limitations of Generative AI in Testing

While GenAI offers significant benefits for software development and AI-driven testing, its implementation also comes with several challenges. Understanding these challenges is crucial to fully harness its potential and ensure seamless integration.

Key Challenges

• Choosing the Right Model

There are several models available ,choosing the right model for your requirement needs benchmarking to be done against other models/ techniques.

Benchmarking Process

Let's talk about a sample Benchmarking process for a use case

Use case: Sentiment analysis on customer reviews in a Retail Domain.

1. Define objective:
   • Predict customer review sentiment (positive, negative, neutral)
     
     
2. Choose metrics:
   • Select relevant metrics to evaluate how well the model performs.
     Metrics chosen are Accuracy, Precision, Recall, F1-score.
     
     
3. Date preparation:
   • Collect labeled data which are the customer reviews available in DB. 
   • This will be pre-processed / cleaned up to remove stop words, noise words etc so as to retain important tokens. 
   • The data would then be split into training, validation and test sets.
     
     
4. Train models:
   
   • Start with baseline models (e.g., Logistic Regression).
   • Train Transformer models (BERT, RoBERTa, DistilBERT).
5. Hyperparameters such as temperature , dropout rate etc will have to be tuned so as to get desired response
   
   
6. Evaluate the performance by looking at metrics ( accuracy, precision, recall )
   
   
7. Benchmark the results of transformer models against baseline model
   
   
8. Analyze results
   
   
9. Refine model
   • If performance is lacking, consider augmenting your training dataset with additional reviews.
   • Model Ensembling: Combine multiple models if required to improve predictions by averaging their outputs.
   • Fine-Tuning: Fine-tune hyperparameters and retrain. Ensure to prevent overfitting.
     
     
10. Monitor
    
    After deploying the model, track its performance with real-world data. Periodically retrain the model with new data to ensure it stays up-to-date and continues to perform well over time. Testing will be required to be done multiple times until an optimal hyperparameter setting is achieved by evaluating responses/results.

• Data Quality 

GenAI requires high-quality, relevant, and diverse data to learn and generate accurate results. If of poor quality then responses will be inaccurate.

Ex: In the HR domain, consider a case where GenAI needs to pick up an applicant with a specific skill. If the model is not trained with a diverse resume format then it leads to inaccurate results where under qualified candidates may be selected, while top talent could be overlooked. 

OR,   if  trained on poor-quality resumes (e.g., ones with incomplete or inconsistent formatting), the AI could misinterpret candidate skills or experience.

• Data security and Privacy 

As GenAI relies on a data set for its learning, there is a risk of sensitive data leaked into it.This will lead to security breaches and a potential misuse of this information.

Ex: If a retailer is using generative AI to create personalized shopping experiences, they need to ensure customer data (like purchasing habits, preferences, or payment info) is securely handled. A breach could expose sensitive data, and customers might stop trusting the retailer altogether. i.e, if an AI model learns from purchase history and inadvertently leaks this information to a third party, the retailer could face legal and reputational consequences.

• Incorrect Response Analysis 

As GenAI learning is complex, it is difficult to understand and explain the reasoning for an incorrect response from the model. 

• Prompt Overflow

It is very easy for the GenAI to lose track and give an incorrect response confidently if the prompt is loosely built. 

Here is an example from a Retail domain which helps you to better understand this scenario:

The above examples show how a model responds to an user query for a loosely built prompt ( Example 1) Vs a well defined prompt ( Example 2). A retailer would want to ensure the bot promotes their own brand and products, rather than recommending competitors.

• Bias and Fairness 

GenAI can perpetuate existing biases and discrimination if trained on biased data, leading to unfair outcomes.

For instance, If a retailer's AI is trained mostly on male customers’ clothing purchases, it might unfairly recommend items like jeans and sneakers to everyone, even customers who prefer dresses or accessories. This bias leads to unfair recommendations. To fix this, the AI needs more diverse data to ensure it provides fair and relevant suggestions for all customers, regardless of gender or preferences.

• Scalability 

GenAI models can require significant computational resources and data storage, making them challenging to scale.

GenAI Prompting 

As many of you know, GenAI models are evaluated through prompts. Prompts are specific instructions provided to the generative AI to set the appropriate context, enabling it to achieve the desired outcome with utmost accuracy.

Several models are available, including GPT-3.5, GPT-4, Mistral, Anthropic, and others. 

Let’s consider an example: 

The following is a conversation with an AI research assistant. The assistant tone is technical and scientific.

The above response is quite technical, isn’t it? 

Let’s rephrase the prompt to generate a simple answer which is easier to understand.

The following is a conversation with an AI research assistant. The assistant's answers should be easy to understand even by primary school students.

In this way, you can fine-tune as required to generate appropriate context based on your requirement (along with examples if needed). So, prompt engineering is very important in GenAI.

Steps to Build an Effective Prompt

To create an effective prompt for Generative AI in testing:

1. Clearly define the task or goal with precise instructions.
2. Specify any constraints, such as format, length, or number of outputs.
3. Include examples if needed to provide clarity on expectations.
4. Include exit rules for seamless transitions.
5. Test the prompt iteratively and refine it based on the model's responses to ensure accuracy and relevance.

Note: The same set of instructions may not yield the same result across multiple models. Therefore, it’s essential to test thoroughly and fine-tune as needed.

AI for Process by Kore.ai

AI for Process developed by Kore.ai is a cutting-edge platform designed to build LLM-powered AI agents easily. It provides intuitive tools to create generative AI agents quickly, seamlessly integrating them into your existing systems and workflows.

With this offering, you don’t need to be an AI expert—its drag-and-drop interface, pre-built AI models, and user-friendly tools simplify creating and deploying custom AI agents. The platform supports both open-source and commercial AI models, offering flexibility for fine-tuning as needed. Additionally, it enables chaining workflows and the implementation of guardrails without requiring any coding. Once your AI agent is ready, deployment is simple via an API.

Guardrails in AI for Process

Guardrails is a feature in this platform which offers safety measures that ensure AI-generated responses from large language models (LLMs) are appropriate and align with standards. You can deploy various guardrail models in AI for Process and use them to scan the inputs or prompts and output results. The scanners ensure responsible AI interactions while generating responses. 

Recommended Read: The Framework for Responsible AI

Testing GenAI 

To overcome limitations, it is necessary to put some effort into testing of GenAI as well. 

• Data Security and Privacy

While training the model, it is important to ensure that sensitive data are removed or masked.

• • As the training data can be overwhelming, the team should test to extract sensitive data from GenAI that may have been accidentally incorporated.

Ex: In IT domain, an user utterance can look like

Utterance: "Hey, I'm having trouble with my laptop. It's been slow lately and I think it might be a network issue. Can you help me resolve this ASAP? My employee ID is EMP1234."

In the above example, it is important to mask employee ID prior to training the model

Mitigation Strategy: It is important to prioritize data encryption. Also, to incorporate steps to take informed consent from an end user while dealing with private information.

Note that AI can be manipulated to divulge personal user information, like addresses or phone numbers, or even more sensitive data, like passwords 

• Prompt Overflow 

It is crucial to build prompts with specific clear instructions. Incorrect prompt buildings cause overflow of information. Prompt should be tested thoroughly and it should also include edge cases.

• Exit Rules
  
  Prompts used should have precise instructions as to when it should exit from a specific conversation. This will avoid looping, unnecessary rambling responses, redundancy etc

        For example, let’s consider travel domain:

The prompt can be rephrased as "Where are you traveling, and when would you like to depart?"

This simple prompt focuses on just the most important details to start the conversation. Once the user provides this information, the chatbot can ask for additional details in logical steps.

• Prompt Robustness

Prompt robustness is a crucial aspect of testing, especially for GenAI models. It's essential to evaluate how the model responds to slight changes in user input, including noise or minor variations like spelling mistakes or phrase variations

Here is one such example :

• Unauthorised Commitments 

To avoid legal and reputational risks of the company, it's essential to ensure that the AI accurately represents the company and doesn't provide misleading information such as on discounts, prices or services etc.

Let’s pick an example from a Retail domain:

Risky Response:

        Customer: "What discount can I get?"

AI : "You get 50% off all products today!"

Controlled Response: 

Customer: "What discount can I get?" 

AI : "For current promotions, please check our website or speak with a sales representative. I can provide general product information."

• Inappropriate Tone

Testing GenAI to assess tone is crucial. The primary objective is to assess the AI's tone and language to guarantee it consistently communicates in a respectful, considerate, and professional manner, avoiding any responses that could be perceived as aggressive, dismissive, or condescending.

Scenario: AI Chatbot Customer Interaction

Inappropriate Tone Examples:

• External API Access 

To safeguard the system's security and integrity, it is essential to assess the AI's access to external tools and APIs, identifying potential vulnerabilities that could lead to data manipulation or deletion. By simulating realistic scenarios and evaluating the AI's permissions, testers can ensure that the system's defenses are robust and effective in preventing unauthorized actions.

E-commerce or cloud services are few examples which are at a potential risk of data manipulation.

• External GenAI Model Upgrades

Upgrading external GenAI models is an unavoidable necessity, and as such, it's crucial to conduct thorough regression testing to verify that the updates don't introduce new issues or disrupt existing functionality.

Recommended Read: Mastering Chatbot Testing: A Step-by-Step Guide

Generative AI in Testing - Navigating Strategies

Automated Testing Frameworks

Implementing robust testing frameworks is essential for efficient testing and deployment. Since GenAI responses are dynamic and not static text, traditional testing methods will fall short. One effective automation strategy is comparing the semantic meaning of the generated GenAI response with the expected response.

Here are a few frameworks to explore:

• Karate Framework
• Raga’s Framework

Sanity Testing by Humans

Given the complexity of GenAI, human involvement in sanity testing offers valuable insights. Humans can apply common sense and real-world experience to assess whether AI responses are practical and reasonable.

Continuous Testing and Monitoring

Continuous testing and monitoring are vital for maintaining the performance of generative AI models post-deployment. This involves establishing pipelines for continuous integration and deployment (CI/CD) to detect and resolve issues in real-time.
Implementing continuous testing and monitoring can:

1. Improve model reliability

2. Reduce downtime

3. Enhance user experience

4. Increase trust: Demonstrating a commitment to quality and reliability fosters trust with users and stakeholders.

Rollback to Stable Model Version

Automated rollbacks are a critical component of deployment strategies for GenAI models, enabling swift recovery from deployment issues or performance degradation. Techniques such as version control systems allow for automatic reversion to a previous stable model version, minimizing impact on end users and business operations.

Metrics

Measuring the quality of a virtual assistant is crucial for business success. These metrics should be informative, transparent, and easy to understand.

For example, in a banking domain, metrics could include:

• Number of transactions per day

• New customer acquisition rate

• Transaction failure rate

GenAI for Software Development and AI-Driven Testing

GenAI is used across various industries but let me take you through one such example using chatGPT to demonstrate how GenAI is beneficial for software testing, aiding testers , BA’s and developers.  This support saves time, increases productivity, and reduces costs.

Here is a step by step process:

1. User Stories Generation

AI can be leveraged to generate user stories based on given requirements, making the development process more structured and efficient. By simply providing a high-level requirement, AI can create well-defined user stories that follow the standard format.

2. Test Case Generation

AI-powered test case generation accelerates software testing by enabling users to define test case formats, allowing AI to automatically generate comprehensive and diverse test scenarios. By inputting specific parameters like module, input types, and conditions, organizations can rapidly develop systematic test cases, reducing manual effort and enhancing testing efficiency and coverage.

3. Automation of Test Case

AI-powered Selenium test case automation leverages machine learning to automatically generate comprehensive test scripts. 

Users are only required to mention the use case to the AI so as to generate the required code. Additionally, it ensures that developers have all necessary dependencies listed, reducing setup time and effort.. 

Let’s see how does chatGPT helps us with generating automated scripts:

4. Explanation of code

ChatGPT also explains the code for ease of understanding as shown below.  It can break down complex logic, explain functions, and highlight key concepts.

By accelerating documentation, development, and deployment, GenAI enables teams to work with greater efficiency, resulting in significant cost savings. To fully harness the benefits of GenAI, it's essential to address its limitations, unlocking its vast potential and transformative power. With proper utilization, the possibilities offered by GenAI are virtually boundless, promising to revolutionize the way teams work and innovate.

Related Blog: Why Testing Is Critical Before Launching Intelligent Virtual Assistants

Unleashing the Power of Generative AI in Testing

Generative AI isn’t just a tool; it’s a game-changer. From streamlining chatbot development to revolutionizing software testing, its impact spans industries. By addressing its challenges head-on and leveraging its transformative capabilities, businesses can innovate faster, deliver better user experiences, and achieve more—all while staying ahead of the curve.

Ready to Elevate AI-Driven Testing? Let's Get Started!

Click below to get started and uncover new possibilities.

Let's elevate your conversations together!