This article discusses the challenges of navigating technical documentation and the limitations of using ChatGPT. It introduces Mantium’s data integration platform that enhances the accuracy and relevance of responses by importing and transforming data from sources like Readme.io, improving the technical documentation experience and developer education.
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Technical documentation is a critical component of any software product. It guides users, developers, and creators, providing them with the necessary information to understand, use, and maintain the product effectively. However, the experience of navigating through technical documentation can often be challenging. Platforms like Readme.io, while popular, can sometimes offer a less-than-optimal search experience, making it difficult for users to find the information they need. Users often have to navigate multiple pages and sections to find what they want. The search functionality, if present, may not be robust enough to deliver accurate results. This can lead to frustration and wasted time.
Developers require instant access to information when they need it. So, how do we address this issue? Some might suggest employing ChatGPT, as it’s been trained on a wealth of data, possibly encompassing the information found in technical documentation. However, it’s important to remember that ChatGPT’s knowledge cutoff is in September 2021. Also, ChatGPT is notorious for hallucinating and making up information (see the image below). The information presented about Mantium in the image below is somewhat misleading.
Consequently, ChatGPT may lack information about concepts published in the documentation after this period (September 2021). This limitation impairs its ability to offer up-to-date and relevant data, diminishing its effectiveness in dynamic and rapidly changing environments.
AI models like ChatGPT, while impressively powerful, might not be equipped with the contextual understanding of new content in technical documentation. This means that despite their ability to generate human-like text, they might struggle to provide accurate, context-specific responses to user queries about a specific product or service.
To recap, we’re faced with two primary challenges: difficulty locating information in technical documentation and ChatGPT’s limitations due to its knowledge cutoff.
The solution to these issues lies in using Mantium data connectors and the ability to build a ChatGPT plugin to provide contextually relevant information quickly. In the following sections, we’ll delve into this approach.
Mantium, an innovative data integration platform, offers a solution to the problems mentioned above. With the Readme.io data source connector, Mantium can import information from Readme.io into the platform. It can then perform transformations on the datasets to make them more suitable for querying by ChatGPT.
Mantium’s data source connectors offer integration capabilities with various data sources, allowing ChatGPT to tap into a wealth of information. By aggregating and organizing data from these disparate sources, Mantium simplifies the process for ChatGPT to access and utilize information, eliminating the need for complex data handling procedures. Now, you can bring data from your technical documentation hosted in Readme.io and also combine it with data from other knowledge sources such as Notion, Research papers (PDFs), etc using Mantium’s platform.
With the above, you can build a robust knowledge base with concepts about your product and services that ChatGPT or any Large Language Model isn’t trained on.
The image below shows what it looks like after importing data from a Readme.io docs site(Mantium’s Developer site).
After using Mantium to import data from Readme.io (the documentation site), the following process is to prepare the data using transformations like Split Text, Combine Columns, and Generate Embeddings. Once the data is transformed and it is converted to embeddings. Mantium ships the embeddings (numerical representation of the text data) to a managed vector database (Redis).
In the Apps section, you can create a new application using the transformed dataset & embeddings. You can then set up your own plugin in ChatGPT using your Mantium-assigned credentials. Alternatively, you can use Mantium’s official ChatGPT Plugin to access your deployed apps. The latter method is what we recommend as you don’t need OpenAI Plugin developer access.
Finally, you can interact with the plugin in ChatGPT to ask questions, learn more from the documentation, and get relevant responses better than the two responses we got above (see the image below for comparison).
The image below presents an accurate, step-by-step guide on how to build a Mantium Plugin that leverages data from Notion to answer queries from the Mantium documentation.
Here is a step-by-step tutorial on how to build the application described above.
The integration of Mantium data connectors in this case – Readme.io with ChatGPT plugin has changed the way we use technical documentation. By importing and transforming data from Readme.io, we’ve overcome traditional limitations, providing users with accurate and up-to-date responses.
As a result of the ability to incorporate data from diverse sources, a substantial knowledge base has been built, improving the relevance and accuracy of ChatGPT’s responses. This strategy has improved technical documentation’s usability, value, and accessibility, which is a huge advancement for developer education.
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