AI Coaching App
- Durum: Closed
- Ödül: $400
- Alınan Girdiler: 15
- Kazanan: Soumya747
I am looking for a freelancer to develop an AI Coaching App that will provide coaching tips to business owners and real estate agents. The main focus of the coaching will be on sales and marketing. I would prefer the use of a chatbot or whatever works for the AI technology. The ideal candidate should have experience in AI development, chatbot development, and knowledge in sales and marketing. The app should also be user-friendly and easy to navigate. Here is what I want to do https://www.instagram.com/reel/CsE5j9NLC6h/?igshid=MzRlODBiNWFlZA==. I got this from ChatGPT
Creating an AI model like "CoachAI", which mimics your personal coaching style, voice, and advice, would be a complex project that involves multiple steps. This includes gathering and preprocessing data, training and tuning models, deploying the model, and then continuously monitoring and improving it. Here's a rough step-by-step plan:
Collect all available data sources: This includes Clubhouse audio, SMS, YouTube videos, and any other digital content where you provide real estate coaching.
Transcribe audio and video content: AI models typically work with text data, so you'll need to transcribe any audio or video content. There are various transcription services available, both manual and automated.
Data Processing and Preparation:
Clean the data: This involves removing any irrelevant information, correcting errors, and ensuring the transcriptions accurately reflect the original audio/video content.
Annotate the data: Depending on the type of model you're building, you might need to annotate the data. For example, if you're building a model that responds to specific types of questions, you could annotate your past responses to those questions.
Model Selection and Training:
Choose an appropriate model architecture: This could be a transformer-based model like GPT-4, or another suitable model depending on your requirements.
Pretrain the model: Start by training the model on a large corpus of text, such as the entire internet. This is usually done using unsupervised learning, where the model learns to predict the next word in a sentence.
Fine-tune the model: After pretraining, fine-tune the model on your specific dataset (i.e., your transcriptions and annotations). This will help the model to mimic your coaching style.
Model Testing and Validation:
Test the model: Once the model is trained, you'll need to thoroughly test it to ensure it's working as expected. This can involve manual testing, automated testing, or ideally both.
Validate the model: In addition to testing the model yourself, you'll want to validate it using real-world feedback. This could involve beta testing with a small group of users.
Deploy the model: Once you're confident the model is working well, you can deploy it. This could involve integrating it into a chatbot interface, creating an app, or any other interface that your users will interact with.
Model Monitoring and Continuous Improvement:
Monitor the model's performance: After deployment, it's important to continuously monitor the model to ensure it's working as expected. This could involve tracking metrics like user satisfaction and model accuracy.
Continuously improve the model: Based on the feedback and metrics you gather, you'll likely want to periodically update the model to improve its performance. This could involve retraining it on new data, adjusting the model architecture, or tuning hyperparameters.
Remember, this is a high-level plan and the actual steps can be more complex and might require deep technical expertise in areas like machine learning, natural language processing, and software engineering. You might also need to address other considerations such as privacy and ethical concerns, especially when working with personal and potentially sensitive data.