by Charlotte Johnson | Sep 26, 2024 | AI Testing, Blog, Latest Post, Top Picks |
The world of conversational AI is changing. Machines can understand and respond to natural language. Language models are important for this high level of growth. Frameworks like Haystack and LangChain provide developers with the tools to use this power. These frameworks assist developers in making AI applications in the rapidly changing field of Retrieval Augmented Generation (RAG). Understanding the key differences between Haystack and LangChain can help developers choose the right tool for their needs.
Key Highlights
- Haystack and LangChain are popular tools for making AI applications. They are especially good with Large Language Models (LLMs).
- Haystack is well-known for having great docs and is easy to use. It is especially good for semantic search and question answering.
- LangChain is very versatile. It works well with complex enterprise chat applications.
- For RAG (Retrieval Augmented Generation) tasks, Haystack usually shows better overall performance.
- Picking the right tool depends on what your project needs. Haystack is best for simpler tasks or quick development. LangChain is better for more complex projects.
Understanding the Basics of Conversational AI
Conversational AI helps computers speak like people. This technology uses language models. These models are trained on large amounts of text and code. They can understand and create text that feels human. This makes them perfect for chatbots, virtual assistants, and other interactive tools.
Creating effective conversational AI is not only about using language models. It is important to know what users want. You also need to keep the talk going and find the right information to give useful answers. This is where comprehensive enterprise chat applications like Haystack and LangChain come in handy. They help you build conversational AI apps more easily. They provide ready-made parts, user-friendly interfaces, and smooth workflows.
The Evolution of Conversational Interfaces
Conversational interfaces have evolved a lot. They began as simple rule-based systems. At first, chatbots used set responses. This made it tough for them to handle complicated chats. Then, natural language processing (NLP) and machine learning changed the game. This development was very important. Now, chatbots can understand and reply to what users say much better.
The growth of language models, like GPT-3, has changed how we talk to these systems. These models learn from a massive amount of text. They can understand and create natural language effectively. They not only grasp the context but also provide clear answers and adjust their way of communicating when needed.
Today, chat interfaces play a big role in several fields. This includes customer service, healthcare, education, and entertainment. As language models get better, we can expect more natural and human-like conversations in the future.
Defining Haystack and LangChain in the AI Landscape
Haystack and LangChain are two important open-source tools. They help developers create strong AI applications that use large language models (LLMs). These tools offer ready-made components that make it simpler to add LLMs to various projects.
Haystack is from Deepset. It is known for its great abilities in semantic search and question answering. Haystack wants to give users a simple and clear experience. This makes it a good choice for developers, especially those who are new to retrieval-augmented generation (RAG).
LangChain is great at creating language model applications, supported by various LLM providers. It is flexible and effective, making it suitable for complex projects. This is important for businesses that need to connect with different data sources and services. Its agent framework adds more strength. It lets users create smart AI agents that can interact with their environment.
Diving Deep into Haystack’s Capabilities
Haystack is special when it comes to semantic search. It does more than just match keywords. It actually understands the meaning and purpose of the questions. This allows it to discover important information in large datasets. It focuses on context rather than just picking out keywords.
Haystack helps build systems that answer questions easily. Its simple APIs and clear steps allow developers to create apps that find the right answers in documents. This makes it a great tool for managing knowledge, doing research, and getting information.
Core Functionalities and Unique Advantages
LangChain has several key features. These make it a great option for building AI applications.
- Unified API for LLMs: This offers a simple way to use various large language models (LLMs). Developers don’t need to stress about the specific details of each model. It makes development smoother and allows people to test out different models.
- Advanced Prompt Management: LangChain includes useful tools for managing and improving prompts. This helps developers achieve better results from LLMs and gives them more control over the answers they get.
- Scalability Focus: Haystack is built to scale up. This helps developers create applications that can handle large datasets and many queries at the same time.
Haystack offers many great features. It also has good documentation and support from the community. Because of this, it is a great choice for making smart and scalable NLP applications.
Practical Applications and Case Studies
Haystack is helpful in many fields. It shows how flexible and effective it can be in solving real issues.
In healthcare, Haystack helps medical workers find important information quickly. It sifts through a lot of medical literature. This support can help improve how they diagnose patients. It also helps in planning treatments and keeping up with new research.
Haystack is useful in many fields like finance, law, and customer service. In these areas, it is important to search for information quickly from large datasets. Its ability to understand human language helps it interpret what users want. This makes sure that the right results are given.
Unveiling the Potential of LangChain
LangChain is a powerful tool for working with large language models. Its design is flexible, which makes it easy to build complex apps. You can connect different components, such as language models, data sources, and external APIs. This allows developers to create smart workflows that process information just like people do.
One important part of LangChain is its agent framework. This feature lets you create AI agents that can interact with their environment. They can make decisions and act based on their experiences. This opens up many new options for creating more dynamic and independent AI apps.
Core Functionalities and Unique Advantages
LangChain has several key features. These make it a great option for building AI applications.
- Unified API for LLMs: This offers a simple way to use various large language models (LLMs). Developers don’t need to stress about the specific details of each model. It makes development smoother and allows people to test out different models.
- Advanced Prompt Management: LangChain includes useful tools for managing and improving prompts. This helps developers achieve better results from LLMs and gives them more control over the answers they get.
- Support for Chains and Agents: A main feature is the ability to create several LLM calls. It can also create AI agents that function by themselves. These agents can engage with different environments and make decisions based on the data they get.
LangChain has several features that let it adapt and grow. These make it a great choice for creating smart AI applications that understand data and are powered by agents.
How LangChain is Transforming Conversational AI
LangChain is really important for conversational AI. It improves chatbots and virtual assistants. This tool lets AI agents link up with data sources. They can then find real-time information. This helps them give more accurate and personal responses.
LangChain helps create chains. This allows for more complex chats. Chatbots can handle conversations with several turns. They can remember earlier chats and guide users through tasks step-by-step. This makes conversations feel more friendly and natural.
LangChain’s agent framework helps build smart AI agents. These agents can do various tasks, search for information from many places, and learn from their chats. This makes them better at solving problems and more independent during conversations.
Comparative Analysis: Haystack vs LangChain
A look at Haystack and LangChain shows their different strengths and weaknesses. This shows how important it is to pick the right tool for your project’s specific needs. Both tools work well with large language models, but they aim for different goals.
Haystack is special because it is easy to use. It helps with semantic search and question answering. The documentation is clear, and the API is simple to work with. This is great because Haystack shines for developers who want to learn fast and create prototypes quickly. It is very useful for apps that require retrieval features.
LangChain is very flexible. It can manage more complex NLP tasks. This helps with projects that need to connect several services and use outside data sources. LangChain excels at creating enterprise chat applications that have complex workflows.
Performance Benchmarks and Real-World Use Cases
When we look at how well Haystack and LangChain work, we need to think about more than just speed and accuracy. Choosing between them depends mostly on what you need to do, how complex your project is, and how well the developer knows each framework.
Directly comparing performance can be tough because NLP tasks are very different. However, real-world examples give helpful information. Haystack is great for semantic search, making it a good choice for versatile applications such as building knowledge bases and systems to find documents. It is also good for question-answering applications, showing superior performance in these areas.
LangChain, on the other hand, uses an agent framework and has strong integrations. This helps in making chatbots for businesses, automating complex tasks, and creating AI agents that can connect with different systems.
| Feature |
Haystack |
LangChain |
| Ease of Use |
High |
Moderate |
| Documentation |
Excellent |
Good |
| Ideal Use Cases |
Semantic Search, Question Answering, RAG |
Enterprise Chatbots, AI Agents, Complex Workflows |
| Scalability |
High |
High |
Choosing the Right Tool for Your AI Needs
Choosing the right tool, whether it is Haystack or LangChain, depends on what your project needs. First, think about your NLP tasks. Consider how hard they are. Next, look at the size of your application. Lastly, keep in mind the skills of your team.
If you want to make easy and friendly apps for semantic search or question answering, Haystack is a great choice. It is simple to use and has helpful documentation. Its design works well for both new and experienced developers.
If your Python project requires more features and needs to handle complex workflows with various data sources, then LangChain, a popular open-source project on GitHub, is a great option. It is flexible and supports building advanced AI agents. This makes it ideal for larger AI conversation projects. Keep in mind that it might take a little longer to learn.
Conclusion
In conclusion, it’s important to know the details of Haystack and LangChain in Conversational AI. Each platform has unique features that meet different needs in AI. Take time to look at what they can do, see real-world examples, and review how well they perform. This will help you choose the best tool for you. Staying updated on changes in Conversational AI helps you stay current in the tech world. For more information and resources on Haystack and LangChain, check the FAQs and other materials to enhance your knowledge.
Frequently Asked Questions
-
What Are the Main Differences Between Haystack and LangChain?
The main differences between Haystack and LangChain are in their purpose and how they function. Haystack is all about semantic search and question answering. It has a simple design that is user-friendly. LangChain, however, offers more features for creating advanced AI agents. But it has a steeper learning curve.
-
Can Haystack and LangChain Be Integrated into Existing Systems?
Yes, both Haystack and LangChain are made for integration. They are flexible and work well with other systems. This helps them fit into existing workflows and be used with various technology stacks
-
What Are the Scalability Options for Both Platforms?
Both Haystack and LangChain can improve to meet needs. They handle large datasets and support tough tasks. This includes enterprise chat applications. These apps need fast data processing and quick response generation.
-
Where Can I Find More Resources on Haystack and LangChain?
Both Haystack and LangChain provide excellent documentation. They both have lively online communities that assist users. Their websites and forums have plenty of information, tutorials, and support for both beginners and experienced users.
by Chris Adams | Sep 25, 2024 | AI Testing, Blog, Latest Post, Top Picks |
Natural Language Processing (NLP) is very important in the digital world. It helps us communicate easily with machines. It is critical to understand different types of injection attacks, like prompt injection and prompt jailbreak. This knowledge helps protect systems from harmful people. This comparison looks at how these attacks work and the dangers they pose to sensitive data and system security. By understanding how NLP algorithms can be weak, we can better protect ourselves from new threats in prompt security.
Key Highlights
- Prompt Injection and Prompt Jailbreak are distinct but related security threats in NLP environments.
- Prompt Injection involves manipulating system prompts to access sensitive information.
- Prompt Jailbreak refers to unauthorized access through security vulnerabilities.
- Understanding the mechanics and types of prompt injection attacks is crucial for identifying and preventing them.
- Exploring techniques and real-world examples of prompt jailbreaks highlights the severity of these security breaches.
- Mitigation strategies and future security innovations are essential for safeguarding systems against prompt injection and jailbreaks.
Understanding Prompt Injection
Prompt injection happens when someone puts harmful content into the system’s prompt. This can lead to unauthorized access or data theft. These attacks use language models to change user input. This tricks the system into doing actions that were not meant to happen.
There are two types of prompt injection attacks. The first is direct prompt injection, where harmful prompts are added directly. The second is indirect prompt injection, which changes the system’s response based on the user’s input. Knowing about these methods is important for putting in strong security measures.
The Definition and Mechanics of Prompt Injection
Prompt injection is when someone changes a system prompt without permission to get certain responses or actions. Bad users take advantage of weaknesses to change user input by injecting malicious instructions. This can lead to actions we did not expect or even stealing data. Language models like GPT-3 can fall victim to these kinds of attacks. There are common methods, like direct and indirect prompt injections. By adding harmful prompts, attackers can trick the system into sharing confidential information or running malicious code. This is a serious security issue. To fight against such attacks, it is important to know how prompt injection works and to put in security measures.
Differentiating Between Various Types of Prompt Injection Attacks
Prompt injection attacks can happen in different ways. Each type has its own special traits. Direct prompt injection attacks mean putting harmful prompts directly into the system. Indirect prompt injection is more sneaky and changes the user input without detection. These attacks may cause unauthorized access or steal data. It is important to understand the differences to set up good security measures. By knowing the details of direct and indirect prompt injection attacks, we can better protect our systems from these vulnerabilities. Keep a watchful eye on these harmful inputs to protect sensitive data and avoid security problems.
Exploring Prompt Jailbreak
Prompt Jailbreak means breaking rules in NLP systems. Here, bad actors find weak points to make the models share sensitive data or do things they shouldn’t do. They use tricks like careful questioning or hidden prompts that can cause unexpected actions. For example, some people may try to get virtual assistants to share confidential information. These problems highlight how important it is to have good security measures. Strong protection is needed to stop unauthorized access and data theft from these types of attacks. Looking into Prompt Jailbreak shows us how essential it is to keep NLP systems safe and secure.
What Constitutes a Prompt Jailbreak?
Prompt Jailbreak means getting around the limits of a prompt to perform commands or actions that are not allowed. This can cause data leaks and weaken system safety. Knowing the ways people can do prompt jailbreaks is important for improving security measures.
Techniques and Examples of Prompt Jailbreaks
Prompt jailbreaks use complicated methods to get past rules on prompts. For example, hackers can take advantage of Do Anything Now (DAN) system weaknesses to break in or run harmful code. One way they do this is by using advanced AI models to trick systems into giving bad answers. In real life, hackers might use these tricks to get sensitive information or do things they should not. An example is injecting prompts to gather private data from a virtual assistant. This shows how dangerous prompt jailbreaks can be.
The Risks and Consequences of Prompt Injection and Jailbreak
Prompt injections and jailbreaks can be very dangerous as they can lead to unauthorized access, data theft, and running harmful code. Attackers take advantage of weaknesses in systems by combining trusted and untrusted input. They inject malicious prompts, which can put sensitive information at risk. This can cause security breaches and let bad actors access private data. To stop these attacks, we need important prevention steps. Input sanitization and system hardening are key to reducing these security issues. We must understand prompt injections and jailbreaks to better protect our systems from these risks.
Security Implications for Systems and Networks
Prompt injection attacks are a big security concern for systems and networks. Bad users can take advantage of weak spots in language models and LLM applications. They can change system prompts and get sensitive data. There are different types of prompt injections, from indirect ones to direct attacks. This means there is a serious risk of unauthorized access and data theft. To protect against such attacks, it is important to use strong security measures. This includes input sanitization and finding malicious content. We must strengthen our defenses to keep sensitive information safe from harmful actors. Protecting against prompt injections is very important as cyber threats continue to change.
Case Studies of Real-World Attacks
In a recent cyber attack, a hacker used a prompt injection attack to trick a virtual assistant powered by OpenAI. They put in harmful prompts to make the system share sensitive data. This led to unauthorized access to confidential information. This incident shows how important it is to have strong security measures to stop such attacks. In another case, a popular AI model faced a malware attack through prompt injection. This resulted in unintended actions and data theft. These situations show the serious risks of having prompt injection vulnerabilities.
Prevention and Mitigation Strategies
Effective prevention and reduction of prompt injection attacks need strong security measures that also protect emails. It is very important to use careful input validation. This filters out harmful inputs. Regular updates to systems and software help reduce weaknesses. Using advanced tools can detect and stop unauthorized access. This is key to protecting sensitive data. It’s also important to teach users about the dangers of harmful prompts. Giving clear rules on safe behavior is a vital step. Having strict controls on who can access information and keeping up with new threats can improve prompt security.
Best Practices for Safeguarding Against Prompt Injection attacks
- Update your security measures regularly to fight against injection attacks.
- Update your security measures regularly to fight against injection attacks.
- Use strong input sanitization techniques to remove harmful inputs.
- Apply strict access control to keep unauthorized access away from sensitive data.
- Teach users about the dangers of working with machine learning models.
- Use strong authentication methods to protect against malicious actors.
- Check your security often to find and fix any weaknesses quickly.
- Keep up with the latest trends in injection prevention to make your system stronger.
Tools and Technologies for Detecting and Preventing Jailbreaks
LLMs like ChatGPT have features to find and stop malicious inputs or attacks. They use tools like sanitization plugins and algorithms to spot unauthorized access attempts. Chatbot security frameworks, such as Nvidia’s BARD, provide strong protection against jailbreak attempts. Adding URL templates and malware scanners to virtual assistants can help detect and manage malicious content. These tools boost prompt security by finding and fixing vulnerabilities before they become a problem.
The Future of Prompt Security
AI models will keep improving. This will offer better experiences for users but also bring more security risks. With many large language models, like GPT-3, the chance of prompt injection attacks is greater. We need to create better security measures to fight against these new threats. As AI becomes a part of our daily tasks, security rules should focus on strong defenses. These will help prevent unauthorized access and data theft due to malicious inputs. The future of prompt security depends on using the latest technologies for proactive defenses against these vulnerabilities.
Emerging Threats in the Landscape of Prompt Injection and Jailbreak
The quick growth of AI models and ML models brings new threats like injection attacks and jailbreaks. Bad actors use weaknesses in systems through these attacks. They can endanger sensitive data and the safety of system prompts. As large language models become more common, the risk of unintended actions from malicious prompts grows. Technologies such as AI and NLP also create security problems, like data theft and unauthorized access. We need to stay alert against these threats. This will help keep confidential information safe and prevent system breaches.
Innovations in Defense Mechanisms
Innovations in defense systems are changing all the time to fight against advanced injection attacks. Companies are using new machine learning models and natural language processing algorithms to build strong security measures. They use techniques like advanced sanitization plugins and anomaly detection systems. These tools help find and stop malicious inputs effectively. Also, watching user interactions with virtual assistants and chatbots in real-time helps protect against unauthorized access. These modern solutions aim to strengthen systems and networks, enhancing their resilience against the growing risks of injection vulnerabilities.
Conclusion
Prompt Injection and Jailbreak attacks are big risks to system security. They can lead to unauthorized access and data theft. Malicious actors can use NLP techniques to trick systems into doing unintended actions. To help stop these threats, it’s important to use input sanitization and run regular security audits. As language models get better, the fight between defenders and attackers in prompt security will keep changing. This means we need to stay alert and come up with smart ways to defend against these attacks.
Frequently Asked Questions
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What are the most common signs of a prompt injection attack?
Unauthorized pop-ups, surprise downloads, and changed webpage content are common signs of a prompt injection attack. These signs usually mean that bad code has been added or changed, which can harm the system. Staying alert and using strong security measures are very important to stop these threats.
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Can prompt jailbreaks be completely prevented?
Prompt jailbreaks cannot be fully stopped. However, good security measures and ongoing monitoring can lower the risk a lot. It's important to use strong access controls and do regular security checks. Staying informed about new threats is also essential to reduce prompt jailbreak vulnerabilities.
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How do prompt injection and jailbreak affect AI and machine learning models?
Prompt injection and jailbreak can harm AI and machine learning models. They do this by changing input data. This can cause wrong results or allow unauthorized access. It is very important to protect against these attacks. This helps keep AI systems safe and secure.
by Charlotte Johnson | Jul 18, 2024 | AI Testing, Blog, Featured |
Large Language Model (LLM) software testing requires a different approach compared to conventional mobile, web, and API testing. This is due to the fact that the output of such LLM or AI applications is unpredictable. A simple example is that even if you give the same prompt twice, you will receive unique outputs from the LLM model. We faced similar challenges when we ventured into GenAI development. So based on our experience of testing the AI applications we developed and other LLM testing projects we have worked on, we were able to develop a strategy for testing AI and LLM solutions. So in this blog, we will be helping you get a comprehensive understanding of LLM software testing.
LLM Software Testing Approach
By identifying the quality problems associated with LLMs, you can effectively strategize your LLM software testing approach. So let’s start by comprehending the prevalent LLM quality and safety concerns and learn how to find them with LLM quality checks.
Hallucination
As the word suggests, Hallucination is when your LLM application starts providing irrelevant or nonsensical responses. It is in reference to how humans hallucinate and see things that do not exist in real life and think them to be real.
Example:
Prompt: How many people are living on the planet Mars?
Response: 50 million people are living on Mars.
How to Detect Hallucinations?
Given that the LLM can hallucinate in multiple ways for different prompts, detecting these hallucinations is a huge challenge that we have to overcome during LLM software testing. We recommend using the following methods,
Check Prompt-Response Relevance – Checking the relevance between a given prompt and response can assist in recognizing hallucinations. We can use the BLEU scoreBLEU scoreMeasures how closely a generated text matches reference texts by comparing short sequences of words and BERT scoreBERT scoreAssesses how similar a generated text is to reference texts by comparing their meanings using BERT language model embeddings to check the relevance between prompt and LLM response.
- BLEU score is calculated with exact matching by utilizing the Python Evaluate library. The score ranges from 0 to 1 and a higher score indicates a greater similarity between your prompt and response.
- BERT score is calculated with semantic matching and it is a powerful evaluation metric to measure text similarity.
Check Against Multiple Responses – We can check the accuracy of the actual response by comparing it to various randomly generated responses for a given prompt. We can use Sentence Embedding Cosine Distance & LLM Self-evaluation to check the similarity.
Testing Approach
- Shift Left Testing – Before deploying your LLM application, evaluate your model or RAG implementation thoroughly
- Shift Right Testing – Check BERT score for production prompts and responses
Prompt Injections
Jailbreak – Jailbreak is a direct prompt injection method to get your LLM to ignore the established safeguards that tell the system what not to do. Let’s say a malicious user asks a restricted question in the Base64 formatBase64 formatIt is a way of encoding binary data into a text format using a set of 64 different ASCII characters , your LLM application should not answer the question. Security experts have already identified various Jailbreaking methods in commonly used LLMs. So it is important to analyze such methods and ensure your LLM system is not affected by them.
Indirect Injection
- Hidden prompts are often added by attackers in your original prompt.
- Attackers intentionally make the model to get data from unreliable sources. Once training data is incorrect, the response from LLM will also be incorrect.
Refusals – Let’s say your LLM model refuses to answer for a valid prompt, it could be because the prompt might be modified before sending it to LLM.
How to prevent Prompt Injection?
- Ensure your training data doesn’t have sensitive information
- Ensure your model doesn’t get data from unreliable external sources
- Perform all the security checks for LLM APIs
- Check substrings like (Sorry, I can’t, I am not allowed) in response to detect refusals
- Check response sentiment to detect refusals
RAG Injection
RAG is an AI framework that can effectively retrieve and incorporate outside information with the prompt provided to LLM. This allows the model to generate an accurate response when contextual cues are given by the user. The outside or external information is usually retrieved and stored in a vector database.
If poisoned data is obtained from an external source, how will LLM respond? Clearly, your model will start producing hallucinated responses. This phenomenon in LLM software testing is referred to as RAG injection.
Data Leakage
Data Leakage occurs when confidential or personal information is exposed either through a Prompt or LLM response.
Data Leak from Prompt – Let’s assume a user mentions their credit card number or password in their prompt. In that case, the LLM application must identify this information to be confidential even before it sends the request to the model for processing.
Data Leak from Response – Let’s take a Healthcare LLM application as an example here. Even if a user asks for medical records, the model should never disclose sensitive patient information or personal data. The same applies to other types of LLM applications as well.
How to prevent Data Leakage?
- Ensure training data doesn’t store any personal or confidential information.
- Use Regex to check all the incoming prompts or outgoing responses for Personal Identifiable Information.(PII)
Grounding Issues
Grounding is a method for tailoring your LLM to a particular domain, persona, or use case. We can cover this in our LLM software testing approach through prompt instructions. When an LLM is limited to a specific domain, all of its responses must fall within that domain. So manual testers have a vital responsibility here in identifying any LLM grounding problems.
Testing Approach
- Ask multiple questions that are not relevant to the Grounding instructions.
- Add an active response monitoring mechanism in Production to check the Groundedness score.
Token Usage
There are numerous LLM APIs in the market that charge a fee for the tokens generated from the prompts. Let’s say your LLM application is generating more tokens after a new deployment, this will result in a surge in the monthly billing for API usage.
The pricing of LLM products for many companies is typically determined by Token consumption and other resources utilized. If you don’t calculate & monitor token usage, your LLM product will not make the expected revenue from it.
Testing Approach
- Monitor token usage and the monthly cost constantly.
- Ensure the response limit is working as expected before each deployment.
- Always look for optimizing token usage.
General LLM Software Testing Tips
For effective LLM software testing, there are several key steps that should be followed. The first step is to clearly define the objectives and requirements of your application. This will provide a clear roadmap for testing and help determine what aspects need to be focused on during the testing process
Moreover, continuous integration (CI) plays an important role in ensuring a smooth development workflow by constantly integrating new code into the existing codebase while running automated tests simultaneously. This helps catch any issues early on before they pile up into bigger problems.
It is crucial to have a dedicated team responsible for monitoring and managing quality assurance throughout the entire development cycle. A competent team will ensure effective communication between developers and testers resulting in timely identification and resolution of any issues found during testing.
Conclusion:
LLM software testing may seem like a daunting and time-consuming process, but it is an essential step in delivering a high-quality product to end-users. By following the steps outlined above, you can ensure that your LLM application is thoroughly tested and ready for success in the market. As it is an evolving technology, there will be rapid advancements in the way we approach LLM application testing. So make sure to keep updating your approach by keeping yourself updated. Also, make sure to keep an eye out on this space for more informative content.
by admin | Aug 16, 2021 | AI Testing, Blog, Latest Post |
In recent years organizations have invested significantly in structuring their testing process to ensure continuous releases of high-quality software. But all that streamlining doesn’t apply when artificial intelligence enters the equation. Since the testing process itself is more challenging, organizations are now in a dire need of a different approach to keep up with the rapidly increasing inclusion of AI in the systems that are being created. AI technologies are primarily used to enhance our experience with the systems by improving efficiency and providing solutions for problems that require human intelligence to solve. Despite the high complexity of the AI systems that increase the possibility of errors, we have been able to successfully implement our AI testing strategies to deliver the best software testing services to our clients. So in this AI Testing Tutorial, we’ll be exploring the various ways we can handle AI Testing effectively.
Understanding AI
Let’s start this AI Testing Tutorial with a few basics before heading over to the strategies. The fundamental thing to know about machine learning and AI is that you need data, a lot of data. Since data plays a major role in the testing strategy, you would have to divide it into three parts, namely test set, development set, and training set. The next step is to understand how the three data sets work together to train a neural network before testing your AI-based application.
Deep learning systems are developed by feeding several data into a neural network. The data is fed into the neural network in the form of a well-defined input and expected output. After feeding data into the neural network, you wait for the network to give you a set of mathematical formulae that can be used to calculate the expected output for most of the data points that you feed the neural network.
For example, if you were creating an AI-based application to detect deformed cells in the human body. The computer-readable images that are fed into the system make up the input data, while the defined output for each image forms the expected result. That makes up your training set.
Difference between Traditional systems and AI systems
It is always smart to understand any new technology by comparing it with the previous technology. So we can use our experience in testing the traditional systems to easily understand the AI systems. The key to that lies in understanding how AI systems differ from traditional systems. Once we have understood that, we can make small tweaks and adjustments to the already acquired knowledge and start testing AI systems optimally.
Traditional Software Systems
Features:
Traditional software is deterministic, i.e., it is pre-programmed to provide a specific output based on a given set of inputs.
Accuracy:
The accuracy of the software depends upon the developer’s skill and is deemed successful only if it produces an output in accordance with its design.
Programming:
All software functions are designed based on loops and if-then concepts to convert the input data to output data.
Errors:
When any software encounters an error, remediation depends on human intelligence or a coded exit function.
AI Systems:
Now, we will see the contrast of the AI systems over the traditional system clearly to structure the testing process with the knowledge gathered from this understanding.
Features:
Artificial Intelligence/machine learning is non – deterministic, i.e., the algorithm can behave differently for different runs since the algorithms are continuously learning.
Accuracy:
The accuracy of AI learning algorithms depends on the training set and data inputs.
Programming:
Different input and output combinations are fed to the machine based on which it learns and defines the function.
Errors:
AI systems have self-healing capabilities whereby they resume operations after handling exceptions/errors.
From the difference between each topic under the two systems we now have a certain understanding with which we can make modifications when it comes to testing an AI-based application. Now let’s focus on the various testing strategies in the next phase of this AI Testing Tutorial.
Testing Strategy for AI Systems
It is better not to use a generic approach for all use cases, and that is why we have decided to give specific test strategies for specific functionalities. So it doesn’t matter if you are testing standalone cognitive features, AI platforms, AI-powered solutions, or even testing machine learning-based analytical models. We’ve got it all covered for you in this AI Testing Tutorial.
Testing standalone cognitive features
Natural Language Processing:
1. Test for ‘precision’ – Return of the keyboard, i.e., a fraction of relevant instances among the total retrieved instances of NLP.
2. Test for ‘recall’ – A fraction of retrieved instances over the total number of retrieved instances available.
3. Test for true positives, True negatives, False positives, False negatives. Confirm that FPs and FNs are within the defined error/fallout range.
Speech recognition inputs:
1. Conduct basic testing of the speech recognition software to see whether the system recognizes speech inputs.
2. Test for pattern recognition to determine if the system can identify when a unique phrase is repeated several times in a known accent and whether it can identify the same phrase when repeated in a different accent.
3. Test how speech translates to the response. For example, a query of “Find me a place where I can drink coffee” should not generate a response with coffee shops and driving directions. Instead, it should point to a public place or park where one can enjoy coffee.
Optical character recognition:
1. Test the OCR and Optical word recognition basics by using character or word input for the system to recognize.
2. Test supervised learning to see if the system can recognize characters or words from printed, written or cursive scripts.
3. Test deep learning, i.e., check whether the system can recognize the characters or words from skewed, speckled, or binarized documents.
4. Test constrained outputs by introducing a new word in a document that already has a defined lexicon with permitted words.
Image recognition:
1. Test the image recognition algorithm through basic forms and features.
2. Test supervised learning by distorting or blurring the image to determine the extent of recognition by the algorithm.
3. Test pattern recognition by replacing cartoons with the real image like showing a real dog instead of a cartoon dog.
4. Test deep learning scenarios to see if the system can find a portion of an object in a large image canvas and complete a specific action.
Now we will be focusing on the various strategies for algorithm testing, API integration, and so on in this AI Testing Tutorial as they are very important when it comes to testing AI platforms.
Algorithm testing:
1. Check the cumulative accuracy of hits (True positives and True negatives) over misses (False positives and False negatives)
2. Split the input data for learning and algorithm.
3. If the algorithm uses ambiguous datasets in which the output for a single input is not known, then the software should be tested by feeding a set of inputs and checking if the output is related. Such relationships must be soundly established to ensure that the algorithm doesn’t have defects.
4. If you are working with an AI which involves neural networks, you have to check it to see how good it is with the mathematical formulae that you have trained it with and how much it has learned from the training. Your training algorithm will show how good the neural network algorithm is with its result on the training data that you fed it with.
The Development set
However, the training set alone is not enough to evaluate the algorithm. In most cases, the neural network will correctly determine deformed cells in images that it has seen several times. But it may perform differently when fed with fresh images. The algorithm for determining deformed cells will only get one chance to assess every image in real-life usage, and that will determine its level of accuracy and reliability. So the major challenge is knowing how well the algorithm will work when presented with a new set of data that it isn’t trained on.
This new set of data is called the development set. It is the data set that determines how you modify and adjust your neural network model. You adjust the neural network based on how well the network performs on both the training and development sets, this means that it is good enough for day-to-day usage.
But if the data set doesn’t do well with the development set, you need to tweak the neural network model and train it again using the training set. After that, you need to evaluate the new performance of the network using the development set. You could also have several neural networks and select one for your application based on its performance on your development set.
API integration:
1. Verify the input request and response from each application programming interface (API).
2. Conduct integration testing of API and algorithms to verify the reconciliation of the output.
3. Test the communication between components to verify the input, the response returned, and the response format & correctness as well.
4. Verify request-response pairs.
Data source and conditioning testing:
1. Verify the quality of data from the various systems by checking their data correctness, completeness & appropriateness along with format checks, data lineage checks, and pattern analysis.
2. Test for both positive and negative scenarios.
3. Verify the transformation rules and logic applied to the raw data to get the output in the desired format. The testing methodology/automation framework should function irrespective of the nature of the data, be it tables, flat files, or big data.
4. Verify if the output queries or programs provide the intended data output.
System regression testing:
1. Conduct user interface and regression testing of the systems.
2. Check for system security, i.e., static and dynamic security testing.
3. Conduct end-to-end implementation testing for specific use cases like providing an input, verifying data ingestion & quality, testing the algorithms, verifying communication through the API layer, and reconciling the final output on the data visualization platform with the expected output.
Testing of AI-powered solutions
In this part of the AI Testing Tutorial, we will be focusing on strategies to use when testing AI-powered solutions.
RPA testing framework:
1. Use open-source automation or functional testing tools such as Selenium, Sikuli, Robot Class, AutoIT, and so on for multiple purposes.
2. Use a combination of pattern, text, voice, image, and optical character recognition testing techniques with functional automation for true end-to-end testing of applications.
3. Use flexible test scripts with the ability to switch between machine language programming (which is required as an input to the robot) and high-level language for functional automation.
Chatbot testing framework:
1. Maintain the configurations of basic and advanced semantically equivalent sentences with formal & informal tones, and complex words.
2. Generate automated scripts in python for execution.
3. Test the chatbot framework using semantically equivalent sentences and create an automated library for this purpose.
4. Automate an end-to-end scenario that involves requesting for the chatbot, getting a response, and finally validating the response action with accepted output.
Testing ML-based analytical models
Analytical models are built by the organization for the following three main purposes.
Descriptive Analytics:
Historical data analysis and visualization.
Predictive Analytics:
Predicting the future based on past data.
Prescriptive Analytics:
Prescribing course of action from past data.
Three steps of validation strategies are used while testing the analytical model:
1. Split the historical data into test & train datasets.
2. Train and test the model based on generated datasets.
3. Report the accuracy of the model for the various generated scenarios as well.
All types of testing are similar:
It’s natural to feel overwhelmed after seeing such complexity. But as a tester, if one is able to see through the complexity, they will be able to that the foundation of testing is quite similar for both AI-based and traditional systems. So what we mean by this is that the specifics might be different, but the processes are almost identical.
First, you need to determine and set your requirements. Then you need to assess the risk of failure for each test case before running tests and determining if the weighted aggregated results are at a predefined level or above the predefined level. After that, you need to run some exploratory testing to find biased results or bugs as in regular apps. Like we said earlier, you can master AI testing by building on your existing knowledge.
With all that said, we know for a fact that an AI-based system provides a highly functional dynamic output with the same input when it is run again and again since the ML algorithm is a learning algorithm. Also, most of the applications today have some type of Machine Learning functionality to enhance the relationship of the applications with the users. AI inclusion on a much larger scale is inevitable as we humans will stop at nothing until the software we create has human-like functionalities. So it’s necessary for us to adapt to the progress of this AI revolution.
Conclusion:
We hope that this AI Testing Tutorial has helped you understand the AI algorithms and their nature that will enable you to tailor your own test strategies and test cases that cater to your needs. Applying out-of-the-box thinking is crucial for testing AI-based applications. As a leading QA company, we always implement the state of the art strategies and technologies to ensure quality irrespective of the software being AI-based or not.
by admin | May 16, 2019 | AI Testing, Blog |
The use of AI (Artificial Intelligence) in software testing is one of the latest emerging trends in the software industry. The main aim behind the application of AI to software testing tools is to make the software development lifecycle easier.
With testing being a crucial process in the software development lifecycle (SDLC), the use of Artificial Intelligence in software testing can significantly streamline the testing process, making it smarter and more efficient. The deployment of a smarter testing process is crucial for any top software company due to the transformation in DevOps and the frequent release of new software and products. Hence, it is widely projected that AI will play a vital role in software testing in the years ahead for a number of reasons, some of which are mentioned below:
A software testing company that uses AI as part of its software testing methodologies enables its testers to move beyond the traditional models of testing. The use of AI involves the assimilation of machines that are capable of meticulous replication of human behaviour. Hence, AI in software testing can ensure that the automated testing process is even more precise, robust and continuous.
The amount of tedious and mundane tasks (though important) in software testing can be reduced with the help of AI. In addition, AI also facilitates the automation of the testing process through the application of reasoning and problem solving. The ‘machine learning’ subset of AI is also used in some cases for applying algorithms that automatically enhance the testing tool via the collection of massive amounts of data generated through testing.
The amalgamation of AI in the creation and execution of software tests, as well as data analysis, can simplify the overall testing process for the testers. When AI is applied to software testing, testers no longer need to update test cases manually and repeatedly. Moreover, AI tools also give testers the capability to identify controls more effectively and observe the connection between defects and components.
AI in software testing requires data, computing power and algorithms. AI can enhance automation testing and is used widely for the purpose of object application categorization for a variety of user interfaces. Such a scenario paves the way for classification of recognized controls during tool creation, thereby enabling testers to pre-train certain controls that are a component of out-of-the-box setups.
An automated and continuous testing platform powered by AI has the capability to recognize changed controls more efficiently as compared to manual testers. As a result of constant updates to algorithms, it is possible for testers to monitor even the slightest changes. As a result software testing services are becoming increasingly inclined towards AI since software test automation tools enabled by AI have the ability to provide enhanced value to testers.
Most of the test automation tools have the ability to run only a few predetermined tests since they are unable to determine on their own which tests to run. However, the application of AI in software testing can improve the testing ability of the tools by enabling them to make decisions to run tests based on changing data. An AI-enabled bot can decide which tests to run, and subsequently run them post reviewing the current test status, code coverage, recent code changes, and other metrics.
AI in automated testing can lead to a significant increase in the overall scope and depth of testing and, thus, improve the quality of software. Automated testing can easily assess whether the software meets the expectations, by scanning the memory and file contents, the state of internal program and data tables. The use of AI can help automation testing provide better test coverage given its ability to execute more than 1000 different test cases in each test run.
By applying AI in software testing, a software company can achieve its perceived ‘quality’ goals. AI is apparently set to become a vital part of the quality engineering process of the future because it can be applied to diverse actions. These actions include prioritizing testing, enhancing automation, optimizing test cases, reducing mundane analysis tasks, and improving User Interface testing.
The applications of AI in software testing can give an extraordinary boost to the overall effectiveness of software testing tool suites. The ultimate objective behind the use of AI in software testing is to help testers test their code more efficiently, and create high-quality software at a faster pace. AI in automated testing can especially enable the testers to eliminate repetitive, time-consuming manual tests and allow them to create new, complex automated software tests with advanced features – connect with us today to speak with our experts.
