Creating a custom ChatGPT for your company – how important is it and who needs it?
A so-called custom ChatGPT is a tailored language model based on the GPT architecture. It provides companies the opportunity to leverage the advanced natural language processing technology directly for their specific needs. In this article, we provide an introduction to custom GPTs: What are they? Who needs them? And what might this look like in practice?
What is a custom ChatGPT?
ChatGPT is specifically known as an interactive chatbot based on the GPT architecture. It represents a clear, specialized application of GPT technology that is easily accessible and directly usable.
When people talk about a custom ChatGPT, they are generally referring to a GPT solution that is tailored to their specific needs.
A custom GPT is a tailored language model specifically designed to meet the needs and data of a company. By training it on internal data, it can efficiently answer employees’ questions. Full control over the model and data also ensures the highest level of data security and privacy.
What are examples of custom GPTs in practice?
Here are some examples of how various organizations use custom GPTs to meet their individual needs:
- A manufacturing company uses a custom GPT to increase employee productivity through an internal AI helpdesk. The AI provides answers based on information from user manuals for large machines, work schedules, and order data. For example: “A warning light appears on the machine. What does it mean and what should I do?”
- An Austrian insurance company uses a custom GPT to get quicker answers to questions about regulations, laws, and internal policies. For example: “Is incident XY covered by insurance? To what extent?” The GPT thus serves as a digital assistant for customer service.
- A state-owned company uses a custom GPT to preserve internal knowledge and prepare it during a wave of retirements. For example: “My employee XY is retiring in 6 months. What information about her tasks is still undocumented?”
When should companies create their own GPT?
Creating a custom ChatGPT can be beneficial for several reasons. Here are some key factors that may influence this decision:
Company-specific knowledge:
Companies can feed their own custom GPT with company-specific data and information, allowing the model to develop a deeper understanding of internal processes, products, and services. This leads to better support for employees and customers..
Tailored responses:
A custom GPT solution can be trained and fine-tuned to cover specific fields of expertise or industries. This allows the delivery of more precise and relevant answers for specialized applications, such as in medicine, law, or engineering.
Data security and control over data:
With a custom ChatGPT, companies maintain full control over their data. This is particularly important in areas where confidential information is processed. Companies can ensure that their data is not accessed or used by third parties.
Independence from third parties:
A custom model gives companies independence from external providers and their terms and conditions. This can be especially advantageous if changes in service conditions or pricing models of providers occur..
Internal training and development
The development and use of a custom GPT promotes internal understanding and competence in dealing with modern AI technologies. This can strengthen the innovation culture within the company.
Data privacy:
By using a custom model, companies can ensure that all data protection policies and legal requirements are met, something that might not always be guaranteed by external providers.
Long-term cost savings:
Although the initial costs of developing and implementing a custom ChatGPT can be high, it can be more cost-efficient in the long run, especially if the model is used extensively. This can reduce dependency on costly external API accesses.
Flexible adaptation:
A custom GPT solution can be flexibly adapted and developed to keep up with the changing needs and requirements of the company. Companies are not dependent on the updates and changes of an external provider..
Advanced features:
A custom ChatGPT offers the possibility to implement experimental features and functions that may not be available in standard solutions. This can lead to innovative applications and competitive advantages..
Experienced readers will notice that one could add a “Yes, but…” to each of these points – the topic is unfortunately not that easy to explain after all. However, we strive to summarize the most important points and are happy to answer any detailed questions ????
How to create your own ChatGPT?
Creating a custom GPT model involves several steps that require technical know-how and appropriate resources. Typically, the competencies for this process are not fully available in-house, and professional GPT providers are brought in.
Here is an overview of the key steps:
1. Requirements analysis and planning: First, goals are defined, and available technical resources, including hardware, data, and specialized personnel, are assessed.
2. Data collection and preparation: Relevant datasets must be collected, cleaned, and formatted to ensure high quality and consistency.
3. Model selection and training: A suitable GPT architecture is chosen, pre-trained models are used, and the model is adjusted with specific data to meet the requirements.
Are we talking about creating an entirely new GPT model or fine-tuning an existing one to internal requirements? This makes a significant difference in the effort required. Starting from scratch would mean training on the contents of the entire internet.
4. Setting up infrastructure: Necessary computing power is provided, and required software and libraries such as TensorFlow or PyTorch are installed.
5. Model training and validation: The model is trained with the collected data, the process is monitored and optimized, and performance is tested with a separate dataset.
6. Implementation and integration: The trained model is implemented into the system environment and integrated into existing applications and workflows.
7. Maintenance and further development: Model performance is continuously monitored, and the model is regularly updated with new data and optimizations.
There are software providers and consultants who assist with the implementation of a private GPT
Us, for example. ONTEC AI provides private GPTs to companies that want to keep their data totally secure and safe.
What alternatives to a custom GPT are there?
Training your own model can often be a very sensible but potentially time-consuming endeavor. Are there alternatives to this?
This brings us to RAGs. At this point, we won’t go into the technical details (we cover that in this introduction to RAG), but we will briefly touch on the differences.
Unlike a custom GPT, a RAG is not specifically trained with data but uses an LLM (already pre-trained standard GPT model) to query different data sources.
Along the way, a RAG can also perform different tests to ensure the resulting answers are as helpful as possible.
Thus, a RAG has some advantages over a GPT:
- Training a GPT takes days, weeks, or months.
- Information in an internal GPT cannot be kept up to date as easily.
- Once a GPT is trained with certain data, it cannot forget that data. If you’re unsatisfied with the data foundation, you need to start over.
- A GPT without a RAG extension can hallucinate without you noticing. Hallucinations from an internal GPT might sound particularly realistic because it can use internal language.
- A GPT can fall victim to prompt injection attacks – with a RAG-based system, this risk is minimized.
Conclusion
The decision to develop a custom GPT for a company offers numerous benefits, from tailored solutions and increased data security to cost efficiency and innovation potential.
However, the initial investments and the need for specialized resources should be carefully considered. Companies willing to take on these challenges can greatly benefit from the long-term advantages of a custom GPT solution.
- A company-owned GPT allows tailored solutions, higher data security, and improved relevance of generated answers through specific adjustments to the company.
- Use cases include personalized customer consultation, internal knowledge management, support in medical diagnoses, and automation of routine tasks.
- Whether creating a custom ChatGPT, a RAG solution, or another system is the ideal solution depends on the specific company’s needs, which should ideally be discussed with trusted AI developers/consultants.