📌 Feedback-Informed Retraining Summary
Feedback-Informed Retraining is a process where systems or models are updated based on feedback about their performance. This feedback can come from users, automated monitoring, or other sources. By retraining using this feedback, the system can improve accuracy, adapt to new requirements, or correct mistakes.
🙋🏻♂️ Explain Feedback-Informed Retraining Simply
Imagine a student taking a test and then getting comments from the teacher on what they got wrong. The student studies those parts again to do better next time. Feedback-Informed Retraining works the same way, helping a computer program learn from its mistakes and get better over time.
📅 How Can it be used?
This method can be used to regularly improve a chatbot by retraining it with real user conversations and feedback.
🗺️ Real World Examples
A customer service chatbot is monitored for incorrect answers. When customers flag wrong responses, developers collect these examples and use them to retrain the chatbot, making its replies more accurate in future conversations.
An online recommendation system for a streaming service gathers user feedback on suggested films. If many users dislike a particular recommendation, the feedback is used to retrain the system so that it offers more relevant suggestions.
✅ FAQ
What is feedback-informed retraining and why is it useful?
Feedback-informed retraining is a way of improving systems or models by updating them using feedback about how well they are working. This feedback might come from everyday users pointing out mistakes, or from automated checks that spot problems. By retraining with this information, the system can become more accurate, fix errors, and keep up with changing needs.
How does feedback from users help improve a system?
When users give feedback, they highlight where a system is working well and where it might be falling short. This real-world feedback helps identify areas that need improvement, so the system can be retrained to address these gaps. Over time, this makes the system more helpful and reliable for everyone.
Can feedback-informed retraining help a system adapt to new situations?
Yes, feedback-informed retraining is especially good for helping systems keep up with new situations or requirements. As users interact with the system and provide feedback, the system can learn from these experiences and adjust itself, which means it does not get stuck with outdated knowledge or habits.
📚 Categories
🔗 External Reference Links
Feedback-Informed Retraining link
👏 Was This Helpful?
If this page helped you, please consider giving us a linkback or share on social media!
📎https://www.efficiencyai.co.uk/knowledge_card/feedback-informed-retraining
Ready to Transform, and Optimise?
At EfficiencyAI, we don’t just understand technology — we understand how it impacts real business operations. Our consultants have delivered global transformation programmes, run strategic workshops, and helped organisations improve processes, automate workflows, and drive measurable results.
Whether you're exploring AI, automation, or data strategy, we bring the experience to guide you from challenge to solution.
Let’s talk about what’s next for your organisation.
💡Other Useful Knowledge Cards
Virtual Interview Tool
A virtual interview tool is a software application that enables job interviews to be conducted remotely using video, audio, or chat. It often includes features like scheduling, automated interview questions, and recording for later review. These tools help employers and candidates connect from different locations without needing to meet in person.
Token Distribution Models
Token distribution models describe the ways digital tokens are allocated to users, investors, team members or the public in blockchain and cryptocurrency projects. These models determine who receives tokens, how many they get, and when they are distributed. Choices about distribution can affect a project's fairness, funding, security and long-term success.
Digital Strategy Realisation
Digital strategy realisation is the process of turning a companynulls digital plans and goals into actual actions and results. It involves putting digital tools, technologies, and processes in place to improve how a business operates and delivers value. This means moving from planning to making changes that help the business compete and grow using digital methods.
AI for Dashboarding
AI for Dashboarding refers to using artificial intelligence to help create, manage, or improve data dashboards. These dashboards display important information in a visual format, helping people understand and track key metrics quickly. By applying AI, dashboards can automatically highlight trends, suggest helpful visuals, or even predict future outcomes based on the data shown.
Container Orchestration
Container orchestration is the automated management of software containers, which are small, self-contained packages that hold an application and everything it needs to run. Orchestration tools help handle tasks such as starting, stopping, and moving containers, as well as monitoring their health and scaling them up or down based on demand. This makes it easier for teams to run complex applications that need to work reliably across many computers or in the cloud.