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Harnessing OpenAI’s Latest No-Code GPT Models: A Data Training Primer

With OpenAI’s unveiling of customizable, no-code GPTs for specialized applications, the question arises: How can organizations effectively train these models with quality data?



The unveiling at OpenAI’s DevDay 2023 was a glimpse into the future of conversational AI. ChatGPT, one of the most popular Large Language Models (LLMs), is poised to leap from popular experiment to practical, customizable tool for a myriad of bespoke applications. While the platform is currently in open beta preview, they teased a new no-code platform to build custom GPTs for different use cases, and a public “app store” experience to share and purchase GPTs that others have built. 

Yet, the allure of crafting a personalized GPT in mere minutes belies the complexity of ensuring its reliability. Beyond the intuitive drag-and-drop interfaces lies the critical task of training these models — not just with sample dialogues, but with vast, nuanced datasets. OpenAI’s no-code promise extends into this realm, suggesting a future where training a GPT model is as straightforward as using it. At the end of the day, a GPT model is only as good as the data it’s trained on.

This blog explores the bridge between the simplicity of no-code model creation and the sophistication required to train them effectively, ensuring that these models do more than converse — they comprehend, assist, and innovate in the specific contexts they were created for.

Potential Use Cases for Custom GPTs

Developers across the internet have already jumped on the OpenAI announcement, imagining what these custom GPTs might be used for and how they can help companies. Rather than being trained on all the data available on the internet as ChatGPT 4.0 is now, these custom GPTs can be securely trained on company’s data that they feed and choose to share. Therefore, custom GPTs can be potentially created for real use cases that seemed infeasible just days ago.


Training Dataset Custom GPT
Internal knowledge base and documents for employees Chat bot that can answer questions ranging from HR inquiries to learning about past projects and company history for employees
Customer reviews on different products GPT that knows and understands customer buying habits and can offer insights and suggestions for how to market, advertise, and guide future campaigns
Library of support articles and tutorials for a product Guide to existing and potential additions to a product’s tutorial and support base helping keep consistency and write to fill gaps, understanding how a product works


The Challenge: No-Code Parity for Training Data in GPTs

The introduction of no-code GPTs by OpenAI has sparked a surge of interest and anticipation for their potential at an enterprise scale. The distinction between a hobbyist crafting GPTs and a corporation implementing them is significant, with the latter facing unique challenges. An enterprise will need to solve for terabyte and even petabyte scale training datasets across hundreds of systems – not just once, but up to real time as data continuously flows in. Imagine a custom GPT trained on real-time inputs of millions of users every second, every day. This poses a serious engineering challenge that risks the accuracy and usefulness of the GPT.

OpenAI already offers advanced privacy controls for how training data is stored and used, as well as private internal-only enterprise GPTs for sensitive topics (like internal knowledge base) and APIs to feed and train GPTs with private third party data. Yet, the question remains: How can enterprises leverage these APIs with enterprise-scale data to train their custom GPT models? The ability to rapidly create GPTs using a no-code approach on OpenAI’s platform is undoubtedly a leap forward. However, if the subsequent steps—ingesting and processing massive amounts of real-world data—become bottlenecks due to complexity or the need for coding expertise, the initial promise of accessibility is not fully realized. There is a discernible disconnect between the wealth of data enterprises wish to utilize and the ‘API gateway’ to the GPT platform.


The Solution: Nexla + ChatGPT API = No-Code, Simple and Clear Training of Custom GPTs with Rich Data

Nexla makes it easy to build data flows from anywhere into ChatGPT API endpoints, offering an experience with parity to OpenAI’s upcoming no-code GPT builder. Extend the OpenAI platform to include no-code, simple data selection and feed into custom GPTs for an end-to-end experience that covers the most important piece of training AI models: using fresh, complete, reliable training data. Using Nexla, a user can feed data in any format (text, table, database, warehouse, email, etc.) into the ChatGPT API, training a custom model, then deploying that model to stakeholders quickly and with no-code. The resulting model will be trustworthy and transparent with clarity on what data it was trained with through Nexla.

Nexla extends the best parts of OpenAI’s new custom GPT features, adding a no-code, simple solution to training custom GPTs with any data, anywhere.


Build Limitless Custom GPTs Based on Your Real Data

OpenAI’s builder platform promises to revolutionize how businesses can leverage custom GPT models, but there remains a critical hurdle: the process of training these models with real, unprocessed data. In the real world, data is rarely presented in a clean, ready-to-use format suitable for AI consumption. This is where tools like Nexla come into play, bridging the gap between raw data and the streamlined, no-code experience offered by OpenAI’s GPT Builder. That means data from messy, unstructured sources can be used just as easily as tidy databases, unlocking the range of real data companies will want to use to build actionable and accurate custom GPT models.

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