Introduction
Think about the tools you use every day. Now, imagine one of those tools was an AI assistant that didn’t just spit out generic answers but learned from the way you work, adapting itself to your style and helping you tackle your toughest problems. How much easier would your day be with that kind of support?
Maybe you’re juggling multiple projects, always searching for ways to save time or make sense of heaps of data. What if you had instant access to advanced AI technologies, like a personal library that’s always open? What if this AI could help you pull insights from your data faster than ever or automate the tedious tasks that eat up your time?
It might sound a bit futuristic, but this is not sci-fi here. OpenAI’s tools—whether it's a Custom GPT, an API, or an API Assistant—make this possible. You don’t need to be a tech wizard to use these tools; you just need to know where they fit into your world.
So, let’s explore how you can put these tools to work in your projects, what they can do for you, and maybe even uncover some things you hadn’t thought of before.
To bring this to life, let’s use the fictional character of Iron Man (Tony Stark) as a memorable example throughout this blog.
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Now let’s start our journey considering that Tony stark exploring Custom GPT, OpenAI API, and API Assistants to make a more Powerful suit |
Note/Disclaimer: Here Tony Stark is just taken as an example for making this concept memorable, and there is no direct reference or no other relationship to real Iron Man concept.
Understanding the Custom GPT: Your personalized AI assistant/Ally
Custom GPTs are AI models fine-tuned/ pre-trained to excel in specific tasks
Example: How Tony Stark leverages Custom GPT to create his Iron Man suit
Picture Tony Stark in his cluttered lab, with holographic screens flashing data and robotic arms moving around. However, this time, he’s not just talking to J.A.R.V.I.S. - he is using a Custom GPT model to take his suit designs to new heights.
Realtime Use Cases:
Building a Custom GPT: From Concept to Reality
-Building a Custom GPT for E-commerce Test Case Generation
Creating a custom GPT model specifically designed for generating test cases for e-commerce applications, particularly those built on the Magento framework, can significantly streamline the QA process.
Step-by-Step Guide
- Log in to Your OpenAI Account
Ensure you have a ChatGPT Plus or Enterprise account to access the GPT builder.
- Navigate to the GPT Builder
- Go to chat.openai.com and log in.
- In the sidebar, click on Explore GPTs.
- Click on Create.
- Define Initial Instructions (Vision)
In the Create panel, enter your initial instructions for the GPT. Describe the purpose and scope of your custom GPT. For example:
"I want to build a custom GPT for generating test cases for Magento e-commerce applications. The test cases should cover functional, non-functional, edge cases, and negative testing, and should be formatted in a table with columns for TC_ID, TC_NAME, DESCRIPTION, PRIORITY, PRE-REQUISITE, STEPS, EXPECTED RESULT, and TEST DATA."
- Fine-Tune the GPT Behavior
Interact with the GPT in the Preview panel, refining its responses based on your requirements. For instance, ensure it understands Magento-specific functionalities and can generate test cases in the specified tabular format.
- Configure Advanced Settings
- Click on Configure.
- Profile Picture: Optionally, upload a profile picture or generate one using DALLE-3.
- Instructions: Update the instructions to include more detailed guidelines on how the GPT should behave. For example:
- Emphasize clarity and conciseness in test cases.
- Ensure prioritization of critical functionalities.
- Include actionable steps and varied test data.
- Focus on security vulnerabilities.
- Upload Knowledge Files
Enhance the GPT's knowledge by uploading relevant documents, such as:
- Magento Open-Source Release Notes for versions 2.4.4 to 2.4.7.
- Adobe Commerce User Guide Documents.
- Capabilities and Actions
- Enable capabilities such as browsing the web, generating images with DALLE-3, and running code.
- Configure actions to allow the GPT to interact with external APIs or perform specific tasks within the ChatGPT platform.
- Save and Share Your Custom GPT
- Click Save (or Update if you are making changes).
- Select the sharing options: Only me, only people with a link, Public, or Anyone at [your company] (for Enterprise accounts).
- Click Confirm.
Overall, by following the above steps, you can build a powerful custom GPT model tailored to generating effective test cases for Magento ecommerce applications. Emphasize clarity, prioritization, actionable steps, and a focus on security to maximize the utility of your custom GPT. Integrate it with your existing QA tools for a streamlined workflow and continuously refine the model based on feedback to improve its accuracy and effectiveness.
Limitations of Custom GPT:
In our Tony stark example, his AI companion is highly specialized, anticipating his needs in combat, suggesting real-time improvements, and providing tailored advice. Yet, Tony Stark remains unsatisfied with his ultimate AI companion. Why?
- Versatility - Can Custom GPT handle tasks beyond combat and suit functionalities?
- Integration Time - Is the effort to fine-tune the GPT model worth the time?
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Scalability - can a highly specialized AI adapt to different scenarios?
Tony’s concerns are valid. Custom GPTs excel at specialized tasks but may lack versatility and scalability in broader applications. Fine-tuning requires significant time, and even then, ongoing maintenance is necessary to keep the model up-to-date with new challenges.
Understanding OpenAI APIs
OpenAI APIs unlock versatile AI capabilities for enhanced application development. Developers can integrate advanced language models into their applications, enabling a wide range of tasks such as:
- Text generation
- Summary creation
- Language translations
Instead of building a custom model, Tony opts for OpenAI API to access general AI capabilities. By integrating the API into his suit, he enhances its versatility, enabling it to handle tasks like language translation, data analysis, and general problem-solving.
This makes the suit adaptable to a broader range of challenges. However, Tony needs to program specific commands and workflows to ensure the AI effectively assists in various scenarios.
Building OpenAI API tool: Things to consider
Creating a requirement gap analysis tool using OpenAI's API can enhance your ability to identify gaps in requirements and streamline your project management process. Let’s now explore the steps to build such a tool.
Step 1: Research and Brainstorm Your Idea
- Identify the Problem: Determine the specific problem your tool will aim to address. For a requirement gap analysis tool, this could involve identifying missing or incomplete requirements in the project documentation.
- Understand OpenAI Models: Review OpenAI's various models to find the best fit for your tool. For instance, GPT-3 excels in natural language understanding and generating text-based analysis. Dalle-E is ideal for generating images and Codex is used for code generation etc.,
- Market Research: Study existing tools in the market and identify their strengths and limitations. Analyze the pricing strategies and revenue models in the AI tool market.
- Gather Feedback: Engage with peers and potential users to refine the concept and validate its viability.
Step 2: Competitive Analysis
- List Competitors: Identify tools that offer similar services.
- Analyze Features and Feedback: Compare their features, pricing, target audience, and customer feedback.
- Identify Gaps: Find gaps in their offerings that your tool can fill. Use this analysis to differentiate your tool.
Step 3: Define Features
- Basic and Advanced Features: Determine the essential features of your tool. Here are some suggestions:
- Requirement Extraction: Automatically extract requirements from project documents.
- Gap Identification: Highlight missing or incomplete requirements.
- Reporting: Generate comprehensive reports on requirement gaps.
- Integration: Integrate with project management tools for seamless workflow.
- Predictive Analysis: Use AI to predict potential project risks based on requirement gaps.
Step 4: Develop Your Tool
- Select the OpenAI Model: Choose a suitable model like GPT-3, GPT-4 etc.
- Get an API Key: Sign up on the OpenAI website to get your API key.
- Build the Tool: Develop the tool using the chosen model. Here's a basic example in Python:
- Develop a User Interface: Consider using frameworks like Flask for a web interface or Streamlit interface for simple UI or tinkter library to build UI or integrate it into existing project management tools.
Step 5: Test, Debug, and Deploy
- Testing: Thoroughly test your tool with various project documents to ensure it accurately identifies requirement gaps.
- Debugging: Fix any issues identified during testing to ensure the tool is robust.
- Deployment: Deploy the tool on a cloud platform or as a web app for users. Use providers like AWS, GCP, or Azure.
By following these steps, you can create a powerful requirement gap analysis tool using OpenAI's API. Ensure you continuously gather user feedback and improve the tool to meet the evolving needs of your users.
Limitations of OpenAI API
In Tony Stark’s latest iteration, he now has a highly versatile, AI that can handle a broad range of tasks. Yet, Tony is still unsatisfied with his ultimate AI companion. “Guess what?”
- Specificity: Can the general AI offer the specialized advice needed in combat?
- Efficiency: Does it require specific programming and workflows needed for effectiveness?
- Optimization: Is The AI’s performance optimized for critical scenarios?
Tony stark’s thoughts are right. While the OpenAI API provides versatility, its effectiveness in combat is questionable.. The general AI capabilities might not be sufficient in high-stakes situations and optimizing the suit for critical scenarios could demand significant programming and fine-tuning.
Understanding OpenAI API Assistants
Open AI API Assistants come with pre-built features, providing a platform equipped with all models and ready-to-use tools that that simplify the creation of AI-based solutions.. By using APIs to connect to these assistant, we can utilize their capabilities to perform a wide range of tasks and enhance the application’s functionality..
In short, API Assistant is a tool that allows developers to craft and build powerful AI assistants, which can perform multifaceted tasks.
Now lets go back to Tony Stark example. Tony started thinking about OpenAI Assistant API, which offers pre-configured AI solutions designed for specific tasks. These assistants are easy to integrate providing immediate functionality with minimal setup- ideal for ready-to-use solutions.
Tony installs pre-built API Assistants like 'Battle Strategist' for combat scenarios, 'Diagnostics Guru' for real-time suit health checks, and 'Comm Tech' for managing communications with other Avengers. During a critical mission, Jarvis 2.0 (Custom GPT) anticipates an enemy’s move and suggests a counterstrategy based on past battles. The Suit's General AI (OpenAI API) translates an alien language in real-time, helping Tony communicate with a newfound ally. Meanwhile, the Battle Strategist Assistant (API Assistant) provides tactical advice, ensuring Tony and the Avengers can coordinate their attacks effectively.
Understanding how the customized knowledge base works in API assistant
On the left side of image below, we start with a document input, which gets processed and added to the knowledge base. This base consists of two parts: pre-existing OpenAI LLM knowledge and newly uploaded knowledge.
Users can submit queries to the knowledge base, which then retrieves the most relevant information to provide an accurate answer.
To understand the abstracted mode of knowledge retrieval, look into the right-side image, the process involves chunking the document (any format) into smaller pieces. These chunks are then transformed into embeddings—numerical representations that capture the semantic meaning of the text.
These embeddings are stored in a vector database. When a query is made, it's also converted into an embedding and matched against the stored embeddings to find the nearest match.
The result is retrieved from the vector database and presented as the final answer to the user. This method ensures efficient and accurate information retrieval based on semantic similarity.
All these knowledge storages were abstracted in OpenAI assistants for ease of developer in creation of new AI tools.
How to build API Assistants
With OpenAI's latest V2 (Beta) API, creating an AI assistant for generating test cases specialized in ecommerce can significantly enhance the QA process. Following steps walks you through the process to build such an assistant, leveraging OpenAI's powerful language models to streamline test case creation.
Steps to Create and Use an Assistant
Step 1: Set Up the Environment
Install the OpenAI Python library and set your API key:
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openai.api_key = os.environ['OPENAI_API_KEY'] |
Step 2: Create an Assistant
Define your assistant with specific instructions and tools:
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client = openai.OpenAI(api_key=openai.api_key)
tools_assistant = client.beta.assistants.create( name="Ecommerce Test Case Assistant", instructions="You are an assistant specialized in generating test cases for Magento ecommerce applications. Provide test cases in tabular format.", model="gpt-3.5-turbo", temperature=0.5, response_format="auto", tools=[ { "type": "function", "function": { "name": "generate_test_case", "description": "Generate a test case for a given feature", "parameters": { "type": "object", "properties": { "feature": { "type": "string", "description": "The ecommerce feature to generate a test case for" } }, "required": ["feature"] }, } } ] ) |
Step 3: Create a Thread
Initiate a conversation thread for interactions:
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thread = client.beta.threads.create() |
Step 4: Send a Message
Send a message to the assistant within the thread:
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message = client.beta.threads.messages.create( thread_id=thread.id, role="user", content="Generate a test case for the checkout process in Magento." )
print(message) |
Step 5: Run the Thread
Execute the thread and retrieve the assistant’s response:
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run = client.beta.threads.runs.create_and_poll( thread_id=thread.id, assistant_id=tools_assistant.id, )
if run.status == "completed": messages = client.beta.threads.messages.list(thread_id=thread.id) for message in messages: print({"role": message.role, "message": message.content[0].text.value}) else: print("Run status:", run.status) |
Step 6: Submit Tool Outputs
Handle the tool outputs required by the assistant:
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def generate_test_case(feature): # Logic to generate test case for the specified feature return f"Test case for {feature}"
tool_outputs = []
for tool in run.required_action.submit_tool_outputs.tool_calls: if tool.function.name == "generate_test_case": feature = json.loads(tool.function.arguments)["feature"] tool_outputs.append({"tool_call_id": tool.id, "output": generate_test_case(feature)})
if tool_outputs: try: run = client.beta.threads.runs.submit_tool_outputs_and_poll( thread_id=thread.id, run_id=run.id, tool_outputs=tool_outputs ) print("Tool outputs submitted successfully.") except Exception as e: print("Failed to submit tool outputs:", e) else: print("No tool outputs to submit.") |
Step 7: View Assistants
Manage your created assistants on the OpenAI Platform in playground or you can directly access those assistants or threads by using the “Assistant ID” or “Thread ID” in an UI built using the Streamlit or Flask apps.
By following these steps, you can create a specialized AI assistant for generating e-commerce test cases. This assistant can help streamline your QA process, making it more efficient and effective.
How API assistants adding value compared to OpenAI API’s
We do have a section down comparing all the three Open AI tools. So, do we need to include this comparison here?
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Aspect |
Assistants API |
Chat Completions API |
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Initial Setup |
Create an Assistant with defined capabilities. |
No explicit setup of an Assistant is required. |
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Session Management |
Initiate and manage a thread for ongoing conversations. |
No explicit session or thread management; each request is independent. |
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Interaction Handling |
Interact through the Runs API, considering the entire conversation context. |
Send the entire chat history in each request, including system prompts and previous interactions. |
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Context Management |
Persistent context through the thread, suitable for extended conversations. |
Context is provided in each request; best for single interactions or where full context is included each time. |
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Complexity |
More complex setup, offering detailed control and customization. |
Simpler and more straightforward, with less granular control. |
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Ideal Use Cases |
Best for detailed, context-heavy conversational applications. |
Suited for simpler chatbots or applications where each response is standalone. |
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Capabilities |
Advanced capabilities like integration with a code interpreter, online search for information queries, the ability to retrieve knowledge from uploaded files, and function calling. |
Primarily focused on function calling, with less emphasis on extended capabilities beyond generating text responses. |
Limitations of using API Assistants
Let’s get back to Tony Stark’s example- now he has well versatile and multi-functional suite however, he still feels that these API assistants may not be solving his problem. “Guess why?”
- Pre-Built Solutions: Do API Assistants provide the same level of customization?
- Ease of Use: Are they as intuitive and quick to set up as needed?
- Task-Specific Excellence: Do they offer the same specialized help without customization?
How to choose between Custom GPT vs OpenAI API vs API Assistants
Overall, both GPTs and Assistants offer custom instructions and knowledge bases, but their applications differ in scope and target audience. The only difference is GPTs are B2C and Assistant APIs are B2B. So, choose your option wisely based on your needs.
Conclusion:
At its core, AI isn't just about fancy technology—it's about improving how to get things done. Whether you are using a Custom GPT to handle specific tasks, integrating OpenAI's APIs into your projects, or deploying an API Assistant for quick solutions, the goal is to make these tools work for you.
Think of AI as a partner that helps you solve problems, save time, and push the boundaries of what you can achieve. Don’t just think about what AI can do; think about what you can do with it. Give these tools a try, play around, see where they fit in your world, and make them part of your everyday toolkit. Ultimately, AI is not here to replace human effort; it's here to enhance it.
Author Bio
⦁ Name: Keerthi Vaddi
⦁ Position at Encora: Director QA
⦁ Education- B. Tech (Computer Science & Engineering)
⦁ Experience - 13 Years
⦁ LinkedIn Profile: https://www.linkedin.com/in/keerthi-vaddi-730a9a1b9/
