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						</div><p>Artificial Intelligence(AI) has recently become a hot topic across industries transforming sectors like finance, healthcare, education and research. The two of its subfields are Generative AI and Machine Learning(ML), but both of these terms are often confused for one another. we will explore the difference in purpose, techniques and capabilities and tools like <a href="https://keploy.io/">Keploy</a>’s GenAI-powered testing platform makes big difference in software testing.</p>
<h2>What is Generative AI ?</h2>
<p>Generative AI refers to subset of AI models that are capable of generating new data. The models generates this new data from the data which it had been trained on. Instead of just analyzing patterns, they create new content—such as text, images, audio, video, or code.</p>
<h2>What are the types of Generative AI ?</h2>
<ul>
<li>
<p>Generative Adversarial Networks (GANs)</p>
</li>
<li>
<p>Variational Autoencoders (VAEs)</p>
</li>
<li>
<p>Transformer-based models</p>
<p><em>Note: Not all transformer-based models are generative(eg., BERT).</em></p>
</li>
<li>
<p>Diffusion Models</p>
</li>
<li>
<p>Recurrent Neural Networks (RNNs)</p>
</li>
<li>
<p>Autoregressive Models</p>
<p><em>Note: All transformer-based LLMs like GPT are autoregressive.</em></p>
</li>
<li>
<p>Flow-Based Models</p>
</li>
<li>
<p>Hybrid Models</p>
</li>
</ul>
<h2>What is Machine Learning ?</h2>
<p>Machine learning is a field of AI where machines learn to recognize patterns, make prediction, make decision without being explicitly programmed for those specific tasks.</p>
<h2>Types of Machine Learning</h2>
<ul>
<li>
<p><strong>Supervised Learning</strong> – Training the model using labelled data to make prediction or decision on unseen data.</p>
</li>
<li>
<p><strong>Unsupervised Learning</strong> – Training the model using unlabelled data to find patterns.</p>
</li>
<li>
<p><strong>Semi-Supervised Learning</strong> – Using both labelled data and unlabelled data for model training.</p>
</li>
<li>
<p><strong>Reinforcement Learning</strong> – Interacts with environment and model earns rewards for its performance.</p>
</li>
</ul>
<h2>When Traditional Machine Learning is the Better Choice ?</h2>
<ul>
<li>
<p>When the goal is prediction or classification (e.g., fraud detection).</p>
</li>
<li>
<p>When the data is structured and labeled.</p>
</li>
<li>
<p>In low-resource settings where generating new data is unnecessary.</p>
</li>
<li>
<p>When interpretability and explainability are important.</p>
</li>
</ul>
<h2>When Generative AI is the Better Choice ?</h2>
<ul>
<li>
<p>When your use case involves content generation or synthetic data creation.</p>
</li>
<li>
<p>For code generation and debugging suggestions.</p>
</li>
<li>
<p>Creating mock user inputs, test cases and explore edge cases.</p>
</li>
<li>
<p>To customize content like emails, thumbnails and product descriptions.</p>
</li>
</ul>
<h2>When to use Machine Learning and Generative AI together ?</h2>
<ul>
<li>
<p>ML and Generative AI both together can complement each other in many ways such as Software testing, Chatbots, Personalization, Marketing etc.</p>
</li>
<li>
<p>For example we will explore some of the real-time use cases to better understand how ML and Generative AI can work together. In Software testing ML models can predict areas of low test coverage or detect anomalies, while Generative AI can auto-generate test cases, stubs, and mocks—as done by tools like Keploy.</p>
</li>
<li>
<p>In Chatbots, ML can be used to classify the user intents or analyze the sentiments to make the Generative AI responses even more natural and human-like.</p>
</li>
<li>
<p>Using ML we can analyze the user behaviors and with Generative AI we can generate contents more personalized for the users.</p>
</li>
<li>
<p>In marketing comes the great potential of ML and Generative AI, with ML we can forecast churn rate and Generative AI generates content such as email or offers for better customer retention.</p>
</li>
</ul>
<h2>Best use cases for Machine Learning</h2>
<ul>
<li>
<p>Fraud detection systems</p>
</li>
<li>
<p>Recommendation systems (eg., Amazon, Netflix)</p>
</li>
<li>
<p>Predictive analysis</p>
</li>
<li>
<p>Sentiment analysis</p>
</li>
<li>
<p>Pattern recognition (eg., Medical Images)</p>
</li>
</ul>
<h2>Best use cases for Generative AI</h2>
<ul>
<li>
<p>Content creation (eg., Images, Videos)</p>
</li>
<li>
<p>Chatbots and virtual assistants</p>
</li>
<li>
<p>Image resolution scaling</p>
</li>
<li>
<p>Code generation and testing</p>
</li>
<li>
<p>Customer feedback summary (eg., Flipkart)</p>
</li>
</ul>
<h2>How does Keploy’s GenAI Powered Testing Platform Help you Automate Testing ?</h2>
<p>Writing test cases can be time consuming and error-prone therefore keploy leverages Generative AI models to auto-generate test cases from the application source code.</p>
<h3>Want to automate testing using GenAI?</h3>
<p>If you’re looking for a vertical unit testing agent that:</p>
<ul>
<li>
<p>Runs on your infra <strong>OR</strong></p>
</li>
<li>
<p>Uses your API key to test within workflows</p>
</li>
</ul>
<p>…then meet <strong>Keploy-gen!</strong> It leverages LLMs to:</p>
<ol>
<li>
<p>Understand code semantics</p>
</li>
<li>
<p>Generate meaningful unit tests</p>
</li>
</ol>
<h3>Prerequisites</h3>
<p><strong>AI model Setup</strong> – Set the environment variable <strong>API_KEY</strong>.</p>
<p>export API_KEY=xxxx</p>
<p><strong>API_KEY</strong> can be from either of one these:</p>
<ul>
<li>
<p><strong>OpenAI’s GPT-4o</strong> directly <strong>[preferred]</strong>.</p>
</li>
<li>
<p>Alternative LLMs via liteLLM</p>
</li>
<li>
<p>Azure OpenAI</p>
<p>Then you need to install the Keploy locally use the below command :</p>
</li>
</ul>
<pre><code>javascript
 curl --silent -O -L https://keploy.io/install.sh &amp;&amp; source install.sh</code></pre>
<p>Please ensure you’ve set the API key, as mentioned in pre-requisites above:</p>
<pre><code>javascript
export API_KEY=xxxx</code></pre>
<h3><strong>Generating Unit Tests</strong></h3>
<ul>
<li>
<p>Run the following command in the root of your application.</p>
<ul>
<li><strong>For Single Test File:</strong> If you prefer to test a smaller section of your application or to control costs, consider generating tests for a single source and its corresponding test file:</li>
</ul>
</li>
</ul>
<pre><code>javascript
 keploy gen --sourceFilePath=&quot;&lt;path to source file&gt;&quot; --testFilePath=&quot;&lt;path to test file for above source file&gt;&quot; --testCommand=&quot;npm test&quot; --coverageReportPath=&quot;&lt;path to coverage.xml&gt;&quot;</code></pre>
<p>To know more about Keploy-gen, <a href="https://github.com/keploy/keploy/blob/main/README-UnitGen.md">Click here</a></p>
<p>Keploy-gen is an open-source product! If you’re tired of setting up locally, we have an enterprise option where you don’t need to configure anything.</p>
<p>We offer two ways to use it:<br />
1️⃣ <strong>As a PR Agent</strong><br />
2️⃣ <strong>Directly in your VS Code</strong></p>
<p>You can try all enterprise features <strong>completely free</strong>!</p>
<ol>
<li>
<p><a href="https://marketplace.visualstudio.com/items?itemName=Keploy.keployio"><strong>VSCode Extension</strong></a></p>
</li>
<li>
<p><a href="https://github.com/marketplace/keploy"><strong>PR Agent</strong></a></p>
</li>
</ol>
<h2>Key differences between Generative AI vs Machine Learning</h2>
<table>
<thead>
<tr>
<th><strong>Feature</strong></th>
<th><strong>Generative AI</strong></th>
<th><strong>Machine Learning</strong></th>
</tr>
</thead>
<tbody>
<tr>
<td>Response</td>
<td>Generate new data</td>
<td>Predicts or classifies data</td>
</tr>
<tr>
<td>Techniques</td>
<td>GANs, VAEs, Transformers, Diffusion</td>
<td>Regression, Classification, Clustering</td>
</tr>
<tr>
<td>Use Case</td>
<td>Content generation, Chatbots</td>
<td>Pattern recognition, sentiment analysis</td>
</tr>
<tr>
<td>Complexity and Hardware</td>
<td>High (requires large datasets &amp; computationaly expensive)</td>
<td>Varies (from simple to complex) and computations can be moderately expensive compared to Generative models</td>
</tr>
<tr>
<td>Data Dependency</td>
<td>Requires large, diverse datasets</td>
<td>Can work with both structured and unstructured data</td>
</tr>
</tbody>
</table>
<h2>Challenges in Generative AI and Machine Learning</h2>
<p><strong>Challenges in Generative AI</strong></p>
<ul>
<li>
<p>Bias in generated content.</p>
</li>
<li>
<p>High computational cost.</p>
</li>
<li>
<p>Plagiarism.</p>
</li>
<li>
<p>Lack of control over generated outputs.</p>
</li>
</ul>
<p><strong>Challenges in Machine Learning</strong></p>
<ul>
<li>
<p>Data quality and availability.</p>
</li>
<li>
<p>Overfitting or underfitting.</p>
</li>
<li>
<p>Interpretability of models.</p>
</li>
<li>
<p>Scalability in production environments.</p>
</li>
</ul>
<h2><strong>Conclusion</strong></h2>
<p>Machine learning models excels at analyzing patters, make predictions from data and in decision making while GenAI models excels at creating new contents ,data and in simulation. They serve different yet complementary roles in the AI ecosystem.</p>
<p>But choosing the right approach depends on our specific needs whether it’s analyzing data, generating new content, or blending both for smarter solutions.</p>
<p>With innovations like Keploy’s GenAI platform, the boundaries between these technologies are becoming increasingly synergistic, especially in areas like automated software testing.</p>
<h2><strong>FAQs:</strong></h2>
<h3><strong>1. Can Generative AI replace Machine Learning?</strong></h3>
<p>No, Both Generative AI and Machine learning have different use cases. For example ML is commonly used to make prediction and analyze patterns while Generative AI is used for content generation.</p>
<h3><strong>2. </strong>Is Generative AI a type of Machine Learning?**</h3>
<p>Yes, it is a subfield that uses advanced ML techniques like neural networks.</p>
<h3><strong>3. Which is better: Generative AI or ML?</strong></h3>
<p>We cannot derive a conclusion which one is better, it is based on which suits your use case.</p>
<h3>4. <strong>What tools are used for Generative AI?</strong></h3>
<p>Commonly known tools include OpenAI’s <a href="https://keploy.io/blog/community/gpt-4-cost-everything-you-need-to-know-before-getting-started">GPT</a>, Stable Diffusion, and tools like HuggingFace Transformers.</p>
<h3><strong>5. How is Keploy AI different from other AI testing platforms?</strong></h3>
<p>Keploy AI not only generates tests but also verifies the tests generated by AI against your application. Unlike other testing platforms, Keploy ensures that the generated tests are actually validated within the context of your application.</p>
<p>This unique verification step sets Keploy apart from other AI-powered testing platforms.</p>

         
        
                    

                    
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