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The Modern Tech Stack and harnessing the potential of LLM - Blogs


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.

What are Large Language Models?

Large Language Models (LLM) are a type of artificial intelligence model that can mimic human intelligence. Using deep learning techniques and large data sets, it can be pre-trained to generate text-based content. The term “large” in LLM refers to the number of data/values the LLM model can learn. 

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 most significant asset in tech history.

The Impact of LLM Models in Enterprises

The LLM models are sophisticated tools that, when rightly deployed, can make a major impact on businesses by increasing efficiency and effectiveness. It is widely used across industries to train personnel, conduct extensive market research, conduct significant competitor analysis, generate code, as a substitute for labor-intensive tasks, and more.

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 ML Models, 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 ML Ops 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 for building a robust model. 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. 

Industry-wide applications of Large Language Models

Improve the Quality of Resource: LLM can understand human preferences and provide user-relevant searches. Google and Bing are already using LLM.

Increased Efficiency: It can process a large set of unstructured data, and process it more efficiently that can be used for market and competitor analysis.

Conversational AI like ChatGPT are built using LLM models.

Top Benefits of Large Language Models

LLMs are immensely beneficial for businesses. The following are a few major advantages of deploying large language models.  

  1. Content Creation - LLMs are pre-trained to generate content such as blogs, social media, and other digital platforms based on a specific set of keywords. It can also rewrite content to make the existing content more resourceful.
  2. Text-to-speech - It is capable of generating natural text feeds for different languages. 
  3. Increased Efficiency - LLM models are widely used in businesses to automate and streamline tasks that are time-consuming and labor-intensive.  
  4. Conversational AI and Chatbots - LLMs facilitate user experience by enabling natural conversations, which is better than older AIs.
  5. Transparency - The LLMs can conveniently connect with the cloud and various disparate systems in the enterprises, which extends transparency and flexibility.

Limitations of Large Language Models

When adopting LLM, businesses may have to endure a few limitations of LLMs.

  1. Large language models can be expensive to train and maintain, limiting their accessibility for smaller businesses.
  2. After the successful adoption of LLM, the operating costs are high.
  3. In terms of content generation, it can produce biased or inaccurate outputs, requiring careful monitoring and editing.
  4. Once LLMs are deployed, continuous monitoring is essential to maintain performance and behavior, ensuring ongoing quality, which demands constant human resources.


The modern LLM tech stacks are transitioning businesses and transforming the way businesses function. Despite the limitations and challenges, the tech stack is efficient uncovers the highest potential of LLMs, and elevates production in enterprises.

Planning to build a powerful business with the right tech stack? Let’s talk! Write to me at [email protected] to get your business covered.

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