Features
Business Rules Engine
Business Rules Engine
Experimentation refers to the systematic and controlled process of conducting tests, trials, or investigations to gather data, gain insights, or validate hypotheses. We are currently in the an era where language models are transforming the landscape of artificial intelligence, and experimentation emerges as the compass which guides these processes.
With experimentation at the forefront, we equip ourselves with the tools to harness the true potential of these models while navigating the ethical, practical, and creative dimensions that lie ahead.
Here's how Orquesta can help perform experimentation using business rules:
Variance and contexts.
Internal testing.
Getting feedback.
Analytics.
Real-world testing.
Variance and contexts
The first thing worth mentioning is that Orquesta provides variance and contexts for all business rules on the platform. You can easily create a single application in Orquesta and have several prompts with different variance and contexts.
The “variance” is a key characteristics, and it signifies the model's capacity to produce different outputs while maintaining semantic coherence with the input. Contexts on the other hand include refers to the specific pieces of text or content that a user provides as input to the language model in order to generate responses. These contexts serve as the foundation upon which the language model generates coherent and contextually relevant responses. In Orquesta the types of contexts include: environments, country, customer-tier, user-role, vendor, product-category, user-segment etc.
Internal testing
Internal testing, also known as in-house testing or quality assurance (QA), is the process of systematically assessing and evaluating the language model's performance, capabilities, and adherence to predefined quality standards before its deployment or release to external users or real world. You can perform internal testing with your team multiple times on the dev before shipping to production, test new features before pushing it to beta users.
As seen in the screenshot, the environment is set to test, which will make it easier to project managers, prompt managers understand the stage of the experimentation.
Benefits of internal testing include:
Reduced risk: Internal testing reduces the risk of releasing a faulty product or system that could damage the organization's reputation.
Feedback loop: Internal testing establishes a feedback loop between development and testing teams, fostering collaboration and knowledge sharing.
Faster deployment: Catching and resolving issues early accelerates the development process, enabling faster deployment of products or updates.
Cost savings: Identifying and fixing issues internally is often more cost-effective than addressing them after a product is released to external customers.
Improved user experience: By uncovering usability and performance issues, internal testing ensures a better user experience.
Enhanced product quality: Internal testing helps identify and rectify defects early in the development process, resulting in a higher-quality product.
Getting feedbacks
Feedbacks is a very important, it is a critical element that plays a pivotal role in shaping and improving the model's performance, and user experience. Serving different prompts and getting feedbacks then studying it, the scores, response and other metrics are selected and the best can be deployed. All team members or the quality assurance team can check the prompts and give feedbacks.
Common reasons for getting feedback include:
Error Correction: Feedback enables the organization to correct errors and improve the accuracy of language models. Users reporting inaccuracies contribute to model refinement.
Bias detection: Feedback helps in detecting and addressing bias in language models. Users can report instances where the model produces biased or discriminatory content, prompting corrective actions.
Identification of weaknesses: Feedback helps identify weaknesses or limitations in language models. Users can point out instances where the model produces incorrect or biased results.
Ethical evaluation: Feedback provides a channel for ethical evaluation and discussion. It allows teams to address concerns related to content generation and potential misuse.
Analytics
Orquesta’s intuitive dashboard allows you show business rules and metrics, then enrich it with charts and graphs on the platform. You can view your total requests, costs, P50, P99 and the Average score in the chart trendline.
Real-word testing
You can perform experimentation and testing with real world data within Orquesta. Testing with real-world data for the purpose of experimentation is an integral component of data-driven decision-making. Some of the reasons for testing include:
Adaptation to Real Data: Practical testing incorporates real-world data and scenarios, making it easier to adapt language models to the nuances of diverse user inputs and requirements.
Contextual Adaptation: Language models often need to adapt to different contexts and industries. Real world testing in specific domains or industries helps fine-tune the model's responses for more relevant and context-aware output.
User experience enhancement: By simulating real interactions with the language model, practical testing helps in understanding user needs and preferences. This information can be used to tailor the model's responses for a more satisfying user experience.
Wrap up
In conclusion, we talked about how Orquesta can help perform experimentation using business rules, the helpful points to remember are variance and contexts, internal testing, getting of feedback, analytics, and real-world testing.
Check out Orquesta documentation.
Business Rules Engine
Experimentation refers to the systematic and controlled process of conducting tests, trials, or investigations to gather data, gain insights, or validate hypotheses. We are currently in the an era where language models are transforming the landscape of artificial intelligence, and experimentation emerges as the compass which guides these processes.
With experimentation at the forefront, we equip ourselves with the tools to harness the true potential of these models while navigating the ethical, practical, and creative dimensions that lie ahead.
Here's how Orquesta can help perform experimentation using business rules:
Variance and contexts.
Internal testing.
Getting feedback.
Analytics.
Real-world testing.
Variance and contexts
The first thing worth mentioning is that Orquesta provides variance and contexts for all business rules on the platform. You can easily create a single application in Orquesta and have several prompts with different variance and contexts.
The “variance” is a key characteristics, and it signifies the model's capacity to produce different outputs while maintaining semantic coherence with the input. Contexts on the other hand include refers to the specific pieces of text or content that a user provides as input to the language model in order to generate responses. These contexts serve as the foundation upon which the language model generates coherent and contextually relevant responses. In Orquesta the types of contexts include: environments, country, customer-tier, user-role, vendor, product-category, user-segment etc.
Internal testing
Internal testing, also known as in-house testing or quality assurance (QA), is the process of systematically assessing and evaluating the language model's performance, capabilities, and adherence to predefined quality standards before its deployment or release to external users or real world. You can perform internal testing with your team multiple times on the dev before shipping to production, test new features before pushing it to beta users.
As seen in the screenshot, the environment is set to test, which will make it easier to project managers, prompt managers understand the stage of the experimentation.
Benefits of internal testing include:
Reduced risk: Internal testing reduces the risk of releasing a faulty product or system that could damage the organization's reputation.
Feedback loop: Internal testing establishes a feedback loop between development and testing teams, fostering collaboration and knowledge sharing.
Faster deployment: Catching and resolving issues early accelerates the development process, enabling faster deployment of products or updates.
Cost savings: Identifying and fixing issues internally is often more cost-effective than addressing them after a product is released to external customers.
Improved user experience: By uncovering usability and performance issues, internal testing ensures a better user experience.
Enhanced product quality: Internal testing helps identify and rectify defects early in the development process, resulting in a higher-quality product.
Getting feedbacks
Feedbacks is a very important, it is a critical element that plays a pivotal role in shaping and improving the model's performance, and user experience. Serving different prompts and getting feedbacks then studying it, the scores, response and other metrics are selected and the best can be deployed. All team members or the quality assurance team can check the prompts and give feedbacks.
Common reasons for getting feedback include:
Error Correction: Feedback enables the organization to correct errors and improve the accuracy of language models. Users reporting inaccuracies contribute to model refinement.
Bias detection: Feedback helps in detecting and addressing bias in language models. Users can report instances where the model produces biased or discriminatory content, prompting corrective actions.
Identification of weaknesses: Feedback helps identify weaknesses or limitations in language models. Users can point out instances where the model produces incorrect or biased results.
Ethical evaluation: Feedback provides a channel for ethical evaluation and discussion. It allows teams to address concerns related to content generation and potential misuse.
Analytics
Orquesta’s intuitive dashboard allows you show business rules and metrics, then enrich it with charts and graphs on the platform. You can view your total requests, costs, P50, P99 and the Average score in the chart trendline.
Real-word testing
You can perform experimentation and testing with real world data within Orquesta. Testing with real-world data for the purpose of experimentation is an integral component of data-driven decision-making. Some of the reasons for testing include:
Adaptation to Real Data: Practical testing incorporates real-world data and scenarios, making it easier to adapt language models to the nuances of diverse user inputs and requirements.
Contextual Adaptation: Language models often need to adapt to different contexts and industries. Real world testing in specific domains or industries helps fine-tune the model's responses for more relevant and context-aware output.
User experience enhancement: By simulating real interactions with the language model, practical testing helps in understanding user needs and preferences. This information can be used to tailor the model's responses for a more satisfying user experience.
Wrap up
In conclusion, we talked about how Orquesta can help perform experimentation using business rules, the helpful points to remember are variance and contexts, internal testing, getting of feedback, analytics, and real-world testing.
Check out Orquesta documentation.
Business Rules Engine
Experimentation refers to the systematic and controlled process of conducting tests, trials, or investigations to gather data, gain insights, or validate hypotheses. We are currently in the an era where language models are transforming the landscape of artificial intelligence, and experimentation emerges as the compass which guides these processes.
With experimentation at the forefront, we equip ourselves with the tools to harness the true potential of these models while navigating the ethical, practical, and creative dimensions that lie ahead.
Here's how Orquesta can help perform experimentation using business rules:
Variance and contexts.
Internal testing.
Getting feedback.
Analytics.
Real-world testing.
Variance and contexts
The first thing worth mentioning is that Orquesta provides variance and contexts for all business rules on the platform. You can easily create a single application in Orquesta and have several prompts with different variance and contexts.
The “variance” is a key characteristics, and it signifies the model's capacity to produce different outputs while maintaining semantic coherence with the input. Contexts on the other hand include refers to the specific pieces of text or content that a user provides as input to the language model in order to generate responses. These contexts serve as the foundation upon which the language model generates coherent and contextually relevant responses. In Orquesta the types of contexts include: environments, country, customer-tier, user-role, vendor, product-category, user-segment etc.
Internal testing
Internal testing, also known as in-house testing or quality assurance (QA), is the process of systematically assessing and evaluating the language model's performance, capabilities, and adherence to predefined quality standards before its deployment or release to external users or real world. You can perform internal testing with your team multiple times on the dev before shipping to production, test new features before pushing it to beta users.
As seen in the screenshot, the environment is set to test, which will make it easier to project managers, prompt managers understand the stage of the experimentation.
Benefits of internal testing include:
Reduced risk: Internal testing reduces the risk of releasing a faulty product or system that could damage the organization's reputation.
Feedback loop: Internal testing establishes a feedback loop between development and testing teams, fostering collaboration and knowledge sharing.
Faster deployment: Catching and resolving issues early accelerates the development process, enabling faster deployment of products or updates.
Cost savings: Identifying and fixing issues internally is often more cost-effective than addressing them after a product is released to external customers.
Improved user experience: By uncovering usability and performance issues, internal testing ensures a better user experience.
Enhanced product quality: Internal testing helps identify and rectify defects early in the development process, resulting in a higher-quality product.
Getting feedbacks
Feedbacks is a very important, it is a critical element that plays a pivotal role in shaping and improving the model's performance, and user experience. Serving different prompts and getting feedbacks then studying it, the scores, response and other metrics are selected and the best can be deployed. All team members or the quality assurance team can check the prompts and give feedbacks.
Common reasons for getting feedback include:
Error Correction: Feedback enables the organization to correct errors and improve the accuracy of language models. Users reporting inaccuracies contribute to model refinement.
Bias detection: Feedback helps in detecting and addressing bias in language models. Users can report instances where the model produces biased or discriminatory content, prompting corrective actions.
Identification of weaknesses: Feedback helps identify weaknesses or limitations in language models. Users can point out instances where the model produces incorrect or biased results.
Ethical evaluation: Feedback provides a channel for ethical evaluation and discussion. It allows teams to address concerns related to content generation and potential misuse.
Analytics
Orquesta’s intuitive dashboard allows you show business rules and metrics, then enrich it with charts and graphs on the platform. You can view your total requests, costs, P50, P99 and the Average score in the chart trendline.
Real-word testing
You can perform experimentation and testing with real world data within Orquesta. Testing with real-world data for the purpose of experimentation is an integral component of data-driven decision-making. Some of the reasons for testing include:
Adaptation to Real Data: Practical testing incorporates real-world data and scenarios, making it easier to adapt language models to the nuances of diverse user inputs and requirements.
Contextual Adaptation: Language models often need to adapt to different contexts and industries. Real world testing in specific domains or industries helps fine-tune the model's responses for more relevant and context-aware output.
User experience enhancement: By simulating real interactions with the language model, practical testing helps in understanding user needs and preferences. This information can be used to tailor the model's responses for a more satisfying user experience.
Wrap up
In conclusion, we talked about how Orquesta can help perform experimentation using business rules, the helpful points to remember are variance and contexts, internal testing, getting of feedback, analytics, and real-world testing.
Check out Orquesta documentation.
