Unlike traditional machine learning models, LLMs are designed to understand the nuances of human language, including the multiple meanings of words and the complex relationships between them. This allows them to generate text that is indistinguishable from human-written content, making them an essential tool for various applications.
These language models use deep learning techniques to predict the probability of a given sequence of words. Unlike traditional machine learning models, LLMs are designed to understand the nuances of human language, including the multiple meanings of words and the complex relationships between them. This allows them to generate text that is indistinguishable from human-written content, making them an essential tool for various applications.
The advantages of LLMs over traditional machine learning models are significant. LLMs are capable of generating contextually relevant text, making them more effective in handling complex and unpredictable data. They can adapt and improve over time, making them more powerful and effective in handling natural language processing tasks. In contrast, traditional machine learning models rely on pre-defined rules, limiting their ability to handle complex data.
Allganize has recently launched a new product that utilizes its proprietary AI models and OpenAI’s LLMs to generate high-quality generative answers from internal documents. With this innovative solution, the power of GPT can be brought to internal data and documents, making it possible to ask questions and get answers quickly or perform tasks that would otherwise not be possible with an LLM by itself.
The new product, which we have named Alli powered by GPT, has a range of features that can help businesses improve their operations and customer service. For example, beyond answering from internal documents, it can create high-quality chat flows to assist with customer service. This enables businesses to respond to customer queries quickly and efficiently.
Let’s take a look at what it takes to create one. Alli GPT has made it very easy to create a LLM application and it can be tailored for a wide range of use cases such as Q&A auto generation, website answerbots, and perform comparisons such as recommending whether or not a candidate would be a good fit based on a job description. As shown below, this specific app is summarizing a document making it easier for users to quickly get to the heart of the information they need.
LLM applications such as these can be easily built within the Alli dashboard. As shown below, creating a “flow” to perform tasks such as summarizing documents is as simple as dragging and dropping different nodes. If you would like to know more information about our skill flows and no-coding solutions, check out our documentation here.
As shown, Alli GPT allows training and prompt engineering of the LLM to tailor itself to a wide range of use cases across all industries. Due to this feature, Alli GPT is able to be fine-tuned specifically to an enterprise’s internal data. The benefits of this are somewhat self-explanatory but being able to fine-tune the LLM for a specific use case helps reduce the number of hallucinations, off topic answers, and continue to provide high quality, relevant answers every time a question is asked or a task is performed.
Unlock new insights and opportunities with custom-built LLMs tailored to your business use case. Contact our team of experts for consultancy and development needs and take your business to the next level.