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The Modern Tech Stack and harnessing the potential of LLM

The limitations of the NLP tech stack gave birth to the modern LLM tech stack. As businesses evolve as connected, interactive, and data-intensive, business products are powered by a new layer of this modern technology.

The Modern LLM Tech Stack

While the tech stack has a collection of software services used for application development, the modern tech stack has distinguished itself with the ability of conversational skills, making it the biggest asset in the Tech-history. Modern LLM Tech Stack

LLM Tech Stack and the major players


Large Language Models - The Most Crucial Part of Tech Stack


After the evolution of ChatGPT, the large language model (LLM) has opened doors to a specialized type of AI innovation. Large Enterprises and generative AI startups want to either take a foundation model and fine-tune it with their data or pre-train a foundation model for total privacy. The LLMs can be trained on unimaginable amounts of text to understand existing content and generate AI-led content.

Mosaic ML - Train and Deploy Generative AI


Mosaic ML has been purchased by Databricks for a jaw-dropping price of $1.3 billion. This software tool helps to create foundation models in days rather than weeks or fine-tune models using their open-source MPT series models or any pre-trained model for approximately 100K. The logical next step after you have clean data is to create an ML Model, and that remains true for the LLM space as well.

Clear GPT - Empowering Enterprises through LLM


Clear GPT is an enterprise-grade solution that sits within your network to fine-tune any foundation model with your data. It is powered by Clear ML, which is an MLOps platform that helps continuous fine-tuning based on RHLF. Being an Nvidia partner, they have access to all the Nvidia foundation models that can be used as the base for fine-tuning the model

Cohere - Unveils Interactive Features


Cohere provides support for Enterprise LLM with secure deployments in a private cloud, secure cloud partners (AWS, Google, Oracle), or Cohere’s managed cloud. Ready to use high-performance LLM models with options to fine-tune your private data? Data being the foundation and a differentiator for all enterprises/gen AI startups, it is obvious why they have to remain within their premises both for security and compliance as well as to create an IP that provides them a unique advantage.

Vector DB / Semantic Search - For Scalable Similarity Search


While fine-tuning the model can be done, it is expensive to train, it is never real-time and when we use Cloud LLM providers there may not be an option for us to fine-tune the model with our private data. To overcome these challenges in real-time, Retrieval Augmented Generation (RAG) helps. The RAG model takes an input and retrieves a set of relevant text chunks or documents from a source (predominantly from a Vector DB). The documents are concatenated with the original input prompt and fed to the text generator (e.g. LLM), which produces the final output. Retrieving content that is semantically related to the query is paramount for building reliable applications around LLMs. This is where a Vector DB is critical, as it helps store and index vectorized content for the query, which can then be sent to LLMs to generate a human-like response. Some of the major players in this space.

Preprocess Data - Make your data AI friendly


Whether it is a classical ML Model or LLM/Generative AI, having clean data is a prerequisite on which a robust model can be built. While some of the players mentioned above in the custom LLM space have a pre-processing component as part of the stack, there are exclusive players in this that only do this. This is a critical component that needs to be part of any ML training pipeline and it is important in LLM as the cost of training the model is expensive. Make sure to train the models with minimal iterations. 

PII Removal - Compliance and Security


PII is a critical component in the training pipeline, especially when we are dealing with private data both in the enterprise and consumer space. We don't want private data to be used for training and leaked into the model as that violates people’s privacy, affects accuracy, and leads to security attacks against the model. 

Conclusion


The modern LLM tech stacks are transitioning businesses and transforming the way businesses function. This new tech stack is scalable and efficient and uncovers the highest potential of LLMs. Got any questions or need to build a powerful business but are not sure how to choose the right tech stack? Write to me at [email protected] to get your business covered.


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