Custom ChatGPT
Custom ChatGPT: We can use the ChatGPT for your own data and build a customized chatbot for your business, which will answer specific questions with correct answers. It can be adjusted or modified to meet specific needs.
Why use Custom ChatGPT?
For businesses or individual developers, a customized chatbot means they can provide specialized answers, support, or even develop unique features suited for their audience.
How do we do it?
We help you to decide the purpose of your chatbot, and then, with the help of machine learning tools and platforms, train your model. Finally, deploy it on your desired platform.
Large Language Model (LLM) Training A huge machine learning model designed to understand and produce human-like text based on the vast amount of data it has been trained on. Well, do not go with the name here, we can train the Algorithm with your own data, the more the data the better it is, and we can even start with 10-15 pages of data.
Why LLM Training?
With an LLM, chatbots or any text generating systems can provide more accurate, relevant, and human-like responses.
What if we need to build a chatbot for all your staff to answer the questions from employee handbook like
Holidays this year?
Diwali Holidays this year?
How to apply reimbursement?
What is the reimbursement policy?
How do we use LLM?
We help you to train LLM with vast amounts of text. When given a prompt, it finds patterns from its knowledge to produce the most fitting response. It’s like an assistant answering all questions.
Machine Learning: Machine Learning is part of artificial intelligence where computers learn from data without being explicitly programmed.
Why Machine Learning?
Yes, there are AI Tools available for a few things. If you need to solve a particular problem, we need to choose the correct ML Algorithm and implement it for your use case. Systems that utilize machine learning can adapt and improve over time, making them more efficient and accurate in tasks.
How do we do it?
How to start with Machine Learning? Begin by understanding the basics online, then use platforms and tools to practice coding and work with datasets.
Jupyter Notebook
Jupyter Notebook: An interactive platform for writing and executing code, primarily used for data analysis and visualization. Why use a Jupyter Notebook? It provides an interactive environment which is great for learning, testing code snippets, and visualizing data.
SageMaker
This is an Amazon service that helps developers in building, training, and deploying machine learning models.
Why do we use SageMaker?
It simplifies the complex process of machine learning, offering a streamlined platform to build and deploy models. This takes care of all the heavy lifting, and we focus on solving the problems, which is a more effective use of our time.
Frequently asked questions (FAQ):
1. What problems does this solution solve, and for which businesses?
Custom ChatGPT provides businesses with context-specific communication automation and thereby reduces support costs and enhances user experience. It will obtain relevant answers to internal queries (e.g., HR policies) or customer questions with domain-specific accuracy, very well suited for scaling support, onboarding, and engagement.
2. What is unique or competitive about your implementation approach?
We focus on accessible and scalable AIs for SMEs and enterprises. We kick-start the chatbot with a minimal dataset of 10-15 pages, and the chatbot keeps getting improved through iterations. Powerful platforms such as Jupyter Notebook and AWS SageMaker are leveraged, minimizing complexity and solving real issues.
3. How scalable is this solution? Will it grow with datasets and users?
Yes, the solution is modular and cloud-native and scales well with the client's data and user growth. All the key functions, including training, re-training, and mass deployment, are supported very efficiently using SageMaker.
4. What is the process of going from the idea to deployment?
We follow a structured and guided approach concerning going from idea to deployment:
- Identify use case and data.
- Preprocess and clean content.
- Train or fine-tune the model.
- Deploy on the client’s preferable platform.
- Continuously-monitor, update, and refine.
5. How secure is the data used for training these models?
Data security is paramount. Models are trained in secure cloud environments, with options for private or on-prem deployment. We ensure no proprietary data is sent to public APIs without clear consent and are fully compliant with GDPR or HIPAA if required.
6. What opportunities exist for growth in the future?
The future roadmap includes:
- Productizing a self-service chatbot builder
- Voice and multilingual interfaces expansion
- Industry-specific bots (HRBot, MedBot)
- API licensing for third party platforms
Conclusion
Custom chat GPT solutions powered by Large Language Models (LLMs) and Machine Learning can alter the ways businesses engage with their users, employees, or customers. Whether it's about certain queries in an employee handbook or about providing specialized support, a customized chatbot gives right, context-aware responses on-the-fly. With tools like Jupyter Notebook for prototyping and SageMaker for large-scale deployment, construction of smart assistants has become more easily accessible. Here, even training a model with a small amount of data can be used to build a highly effective, efficient, and always-learning solution that truly understands your unique needs.