Large Language Models (LLM) are changing the way developers and QA engineers solve problems. They allow for quicker code generation, debugging, and automated testing, reducing development time by up to 40%. This shift has prompted 67% of senior IT leaders to focus on generative AI, with 33% planning to make it a top priority within the next 18 months.
However, while LLM models offer immense potential, understanding how to get the most out of them while maintaining quality is important. It’s equally essential to be aware of their limitations and ways to navigate them.
In this guide, we’ll provide an overview and address common questions about working with LLM models. This should give you better control of your software development processes.
How Do LLM Models Work?
Large Language Models are advanced neural networks designed to understand and generate human-like text. Their development involves two key stages: pre-training, where they learn language patterns from vast data, and fine-tuning, where their capabilities are refined through specific interactions to produce coherent responses.
At their core, LLMs use a technique called transformer architecture. This approach enables the model to process input text and generate output by simultaneously paying attention to different parts of the input. This parallel processing capability is what allows LLMs to handle context and relationships within text effectively.
LLMs use a token-based system, where tokens can be words, parts of words, or characters. The model processes these tokens sequentially, predicting the most likely next token based on the given context. A notable feature of LLMs is “few-shot learning“, which enables them to adapt to new tasks with minimal additional training by using examples provided within the prompt.
LLM Model Workflow
The LLM workflow involves a series of steps that transform user input into coherent, context-aware responses. Here’s an overview of how these models process and generate text:
- Initial Query: The process starts when a user submits a prompt. The model analyzes the input to understand its context and intent.
- Generation Process: The model generates a response by predicting each word or token based on probabilities derived from its training data.
- Generation Methods: The approach to generating responses can vary, affecting the quality and relevance of the output:
- Greedy Search: This method selects the word with the highest probability at each step. It is fast but might miss better options.
- Beam Search: Considers multiple possible words simultaneously, offering higher-quality responses but requiring more computational resources.
- Sampling: Introduces randomness in word selection, which can foster creativity but may result in less coherent text if not managed well.
- Result: After generating the response, the model performs post-processing tasks such as formatting adjustments, content filtering, and applying specific constraints as needed.
Overall, LLMs refine their outputs through iterative processing, adjusting their responses based on continuous task analysis. This iterative approach helps maintain coherence and context throughout longer texts.
Data Protection and Privacy in AI
Despite the growing interest in LLMs, data privacy and ethical guidelines are a real concern among IT leaders. Here are some essential data protection practices when working with Large Language Models (LLMs):
Personal Identifiable Information (PII)
It’s vital to avoid sharing any personally identifiable information (PII) in your interactions with LLMs. PII includes details such as names, addresses, and contact numbers that can be used to identify individuals. For instance, if you’re testing a customer support chatbot, use placeholder names like “Jane Smith” instead of real names to avoid exposing actual customer data.
Protecting Sensitive Information
Sensitive information like API keys, passwords, or internal tokens should never be disclosed during interactions with LLMs. These pieces of data are critical for accessing and controlling systems and should be protected at all times. For example, if you need to demonstrate a system error or issue, use generic terms rather than revealing actual access credentials.
Anonymization or Use of Dummy Data
When working with examples or scenarios, anonymize real data or use fictitious data to safeguard privacy. This helps avoid inadvertently exposing confidential information. For example, instead of using actual user email addresses, you might use formatted dummy emails like user@example.com to illustrate points in your queries.
Disabling Code Learning
When using plugins or extensions with LLM models, ensure any learning functionalities are turned off. This prevents the model from learning and potentially misusing your proprietary code or confidential data. If a tool has options to save or learn from interactions, review and disable these features if they are not essential.
LLM Use Best Practices
The following best practices will help you make the most effective use of LLM chats in your work.
Assign a Role
Define the AI’s role clearly to tailor responses to your needs. For instance, if you’re working on a mobile app, specify that the AI should act as a mobile developer. This narrows the scope and leads to more relevant and tailored responses. To put it in a different context, specifying to the model that “You are a QA engineer” can guide the model to generate responses aligned with software testing practices.
Define a Clear Goal
Determine your end goal before creating a query. Instead of asking vague questions, be specific. For example, “Help me optimize this function for better performance” is more effective than “How can I improve my code?”
Whether you’re seeking a code snippet or an explanation, a well-defined goal helps you obtain precise answers.
Provide Context and Constraints
Offer important details about your situation or problem. Mention any constraints, such as “This needs to work with Python 3.8”; or, if you’re asking for a SQL query, specify the database type, “Generate a MySQL query to retrieve user data where the email is not null.”
Use Examples and Templates
If you have specific expectations, provide examples or templates. This guides the AI to produce responses in your desired format. For instance, if you need a specific code structure, show a sample of how you want it organized.
Periodically Remind the AI of Context
Over time, the model may lose track of previous interactions. Periodically reminding it of key details ensures continuity in the responses. For example, if you’re in a long session, briefly recap the main task before continuing.
Always Verify the Response
Even when the model seems accurate, always double-check the information before use. Remember, while LLMs are powerful, they can make mistakes; therefore, human oversight is essential for maintaining quality in software delivery. Run code snippets, fact-check information, and ensure the response aligns with your project requirements. This ensures reliability and reduces the risk of errors in your work.
Optimal Strategies for Creating LLM Prompts
Clear communication is essential for accurate and relevant results when engaging with LLM models. Several strategies can help you interact effectively with these models, each suited to different types of tasks and objectives. Here’s a breakdown of some of the most effective approaches.
Least-to-Most Prompting
Begin with simple prompts and gradually increase the complexity of your requests. This step-by-step approach helps manage and refine the output at each stage. Start with straightforward tasks to see how the model responds. For example, if you need a list of email formats, you might start with simple positive examples before asking for negative ones. This lets you correct small errors early and progressively tackle more complex requirements.
Example:
User: Generate 3 positive random emails for email field testing
Assistant: Here are three positive random emails for email field testing: test.user@example.com jane.doe@sample.org john123@domain.net
User: Can you also generate 3 emails for negative cases?
Assistant: Sure! Here are three negative emails for email field testing: test.user@.com, jane.doe@sample, john123@domain..net
User: Now, combine positive and negative emails and generate a JSON with each object’s first and last names.
Assistant: Certainly! Below is a JSON object that combines both positive and negative email examples. Each object includes a firstName, lastName, and an email field.
[
{
“firstName”: “Alex”,
“lastName”: “Jordan”,
“email”: “test.user@example.com”
},
… ]
Self-Ask Prompting
Encourage the model to ask for more information if needed. This method involves setting clear goals and prompting the model to seek clarification when it lacks specific details. This approach ensures that the model can gather the necessary data for complex tasks to deliver accurate results.
Example:
User: I want to test the search field in the user’s table. Ask me questions for generating a checklist for this feature or any missing information if you need.
Assistant: What should the search field support? (e.g., name, email)
User: Name and email.
Assistant: Should the search be case-sensitive? How to handle no matches?
User: Case-insensitive, and display a ‘No results found’ message for no matches.
Assistant: How should partial matches be handled? Any performance requirements for large datasets?. . .
Sequential Prompting
Break down complex tasks into sequential steps. This approach allows the model to build upon previous responses, making it easier to handle intricate tasks. Each step should logically follow from the previous one, allowing the model to refine its output gradually.
Example:
User: Help me with creating a simple calculator in Java.
Assistant: Of course, here is an example of a simple calculator in Java that supports basic arithmetic operations. {{ code }}
User: Can you analyze the written code and add error handling?
Assistant: Of course, let’s add error handling to our calculator. {{ code }}
User: Let’s improve the code and apply OOP principles.
Assistant: Of course! Let’s transform our simple calculator into an object-oriented program (OOP). Here’s the enhanced version of the calculator: {{ code }}
Interactive Prompting
Use an iterative process where you adjust the model’s responses based on real-time feedback. This approach involves engaging in a dialogue with the model, making adjustments as needed, and continually refining the output until it meets your expectations.
Example:
User: I need help with creating documentation for my project. Where should I start?
Assistant: Let’s begin with overview. Can you describe the main purpose of your software?
User: It’s a project management tool.
Assistant: Great. What are the key features of your project management tool that should be highlighted in the documentation?
User: The main features are task tracking, team collaboration, and project timelines.
Assistant: How do you want to organize the documentation? For example, should it include sections like installation, user guides, and FAQs?
ORIL’s AI Expertise and Enablement Services
ORIL specializes in integrating AI solutions into software development and QA processes. Our experts help businesses leverage LLMs and other AI technologies to boost productivity and innovation.
Our services include:
- Generative AI Model Integration: We simplify the adoption of generative AI by integrating third-party AI APIs into your existing applications.
- Custom AI Model Development: We create tailored AI applications based on your specific data and industry needs.
- LLM Fine-Tuning: We optimize AI model performance by customizing them with your data for precise applications.
We also provide AI consulting in discovery, evaluation, strategic planning, and implementation. Our core services extend to product strategy and design, development, digital transformation, dedicated teams, IoT software development, and Salesforce development. We serve clients across industries such as real estate, transportation, healthtech, and fintech.
Conclusion
LLM models like ChatGPT (PM Edition) are powerful tools for software developers and QA engineers. When used effectively, they can significantly boost productivity and innovation. However, it’s crucial to understand their limitations and implement proper data protection measures.
More importantly, as LLM models continue to reshape software development, it’s critical to prioritize security and avoid sharing confidential information. Remember to experiment with various querying approaches to find the most effective combinations for your needs. And don’t forget that LLMs can still produce inaccurate results, so use caution when responses can’t be verified. Finally, stay informed about rapid technological advancements like ChatGPT’s new conversational features and Apple’s partnership with OpenAI.
