2024-10-28 | By Mariusz Jażdżyk
To build AI effectively, it is essential to integrate various elements: understanding user needs, mastering data, developing algorithms, and facilitating effective communication among individuals from different fields.
This is an incredibly challenging task. For those unprepared for such complexities, implementing AI can feel like an insurmountable barrier. As a result, many companies become consumers of AI solutions, while only a select few manage to become creators of these technologies.
Whether a company aspires to be a user or a creator, it is crucial to focus on strategies that enhance the chances of a successful implementation.
Some time ago, while implementing recommendation systems, we made a significant investment in test automation. This allowed us to monitor the quality of the solution on many levels. This is particularly important, as even minor changes to the system can have serious consequences. With complex logic, structured and clear test reports help us significantly in catching these issues.
An essential aspect of AI model development is the tuning of hyperparameters. These are the parameters that govern the behavior and performance of the algorithm itself, not the data it's trained on. Hyperparameters, such as learning rates, batch sizes, and regularization techniques, can dramatically influence the accuracy and efficiency of the model.
Finding the optimal combination of hyperparameters is often a complex, time-consuming process. By automating this process and continuously testing a range of configurations, we improve the performance of our AI systems while reducing the time spent on manual tuning. The ability to quickly adapt hyperparameters is a significant advantage in maintaining model relevance and efficiency.
Solutions based on LLMs are significantly more demanding. The scale of possibilities expands, and the number of potential questions and answers grows exponentially compared to earlier algorithms.
As the saying goes: "If you don’t measure it, you can’t manage it." In our case, AI is tested by AI — much like in the movie WarGames, where machines repeatedly simulated conflict scenarios.
In our approach, one model supervises another. To achieve this, we developed an "arbiter," equipped with key goals and criteria, to analyze the behavior of the system being developed. The arbiter evaluates tens of thousands of test cases based on predefined scenarios. While its assessments may sometimes differ from those of a human, systematic measurement using consistent criteria allows us to make genuine improvements to the algorithms.
As I write this, the Arbiter is hard at work. The result of its analysis will be a report outlining which configurations are effective and which need adjustment. Although final decisions are still made by humans, we can't guarantee it will always remain that way!
Our scoring framework for chatbots focuses on several key criteria, including:
Answer Relevance: This metric evaluates how well the AI-generated responses meet the user's query. A higher relevance score indicates a more accurate and contextually appropriate answer.
Clarity: This measures how clearly the AI conveys its message. Clear communication helps users easily understand the information provided.
Language Quality: This assesses the grammatical correctness, fluency, and coherence of the language used in the responses. Ensuring high language quality is crucial for user satisfaction and comprehension.
Proper Length: This criterion evaluates whether the length of the responses is appropriate for the context, ensuring that answers are neither too brief nor overly verbose.
Customer/User Focus: This metric considers how well the responses align with user intent and needs, reflecting the AI’s ability to prioritize relevant information.
Ethics: This aspect ensures that the responses adhere to ethical standards, avoiding biased or inappropriate content.
Response Time: This metric rewards quicker answers while maintaining quality standards. A faster response time contributes positively to user experience.
By using this comprehensive scoring system, we can effectively monitor the performance of our AI solutions. This automated measure allows us to quickly identify areas for enhancement and ensure our AI solutions deliver high-quality responses to users.
While many companies are still in the early stages of experimentation, we give our algorithms the opportunity to refine each other. Thanks to this process, in the past few days alone, we have incorporated several new models into the project, one of which was rejected, we focused on previously marginal parameters, and we made changes that brought better results.
It’s not easy, but achieving the same goals at lower costs is indeed possible.
Author: Mariusz Jażdżyk
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