Design and development of specialized LLM systems 
By the process of incubating LLM with pe-training methods SFT (Supervised Fine Tuning), DPO (Direct Preference Optimization), and RLHF (Reinforcement Learning with Human Feedback).

Creating the LLM Inference Engine
LLM Inference Engine is a software system. They can take questions or messages from users and then create answers that are expected to meet the questioner's or interlocutor's needs. The LLM Inference Engine system will consist of an API system, incoming and outgoing data filtering system, Vector Database system, KM system, Chatbot system, Dashboard system. Anti-hallucination system, Efficiency enhancement system, we provide services for creating Inference Engines using LLM Llama 2, 3, GPT 3, and Mistral  

Create RAG-based innovations
RAG or Retrieval Augmented Generation or creating a Prompt for the LLM system where the Prompt consists of the question and the questioner's information obtained from the Vector Database to get the answer from LLM that best meets the needs of the person. The creation of the RAG needs to use the operational data of the company and store it in the Vector Database. A user's query will first search the Vector Database to get the best match information, and then the query and the additional information from the Vector Database will form a prompt to the LLM to get the response. RAG uses it in billing, call center, HR, and organizational regulations. and much more.  

NVIDIA Tensor RT-LLM
NVIDIA Tensor RT-LLM is a Python API for creating LLM that can solve problems that are too difficult to solve using GPT4. Tensor RT also helps with optimization, making LLM run faster. Whether using Fash Attention, Infight Batching, and FP8 

Content Architecture for Specialization of LLM
The process of specialization of LLM consists ofPre-training, Supervised Fine Tuning (SFT), Reinforcement Learning with HumanFeedback (RLSF), Direct Preference Generation (DPO), and Retrieval Augmented Generation (RAG). The technical pre-requisites are 
      1) LLM Inference Engine. 
      2) Performance and Benchmarking. 
      3) Technical Infrastructure. 

Create a Pilot Project such as RAG that uses an LLM to respond to a prompt comprising a user's query and relevant factual information. 

Develop a website / Mobile App to provide AI services, AI e-learning for upskill & reskill, and creating a community of AI users in the organization.