AI application such as Claude or chatGPT can connect to data sources, tools [search engine] and workflow [prompts] through MCP and perform a tasks.

MCP like an interface which communicated to MCP client and discover their requirement and offer available services for their requirement. 

Embedding represent text as set of numerical data along with tensors (different dimensions)

Each dimension will store some information about text meaning or syntactical meaning.

Each words or sentence with similar meaning are stored near by vector space.

Models will learn to place a similar words or sentences close  together in the embedded space.

Common pre-trained models such as BERT and RoBERTs are  used for generating an embedding inside of vector space.

We can able to use an embedded for NLP tasks like semantic search, text classification and sentimental analysis.

It is integrate an AI agents to enhance the RAG approach. It will breakdown from complex queries into manageable parts and using API tools where need to augment processing and better result.

Traditional LLM model is refine a dataset into pretraining workflow. The pretraining send a data into fine tuning model and give a precise collected output data. It will send it to human feed back and correct incase of any mismatch with fine tuning model.

    DeepSeek applied supervised fine-tuning to refine the models' capabilities. This involved training on datasets containing reasoning and non-reasoning tasks. Notably, reasoning data was generated by specialized "expert models" trained for specific domains such as mathematics, programming, and logic. These expert models were developed through supervised fine-tuning on both original responses and synthetic data generated by internal models like DeepSeek-R1-Lite. The use of expert models allowed DeepSeek to generate high-quality synthetic reasoning data to enhance the primary model's performance.