Save the world by using RAG &Federated AI

RAG is to prevent Hallucination and incorrect bias in the wrong direction which is one of the key issues of LLMs . When you fine-tune a base model with new data, even if it integrates this new data without overfitting, the issue of hallucinations remains a key challenge for the fine-tuned model

"Hallucination" is basically confidently stating wrong/fake/fabricated data in right/correct/real way making the user asking the question delusional on the response received. this is not replacing fine-tuning?but complimenting it in a better way.

"fine-tuning" is basically taking a base LLM/SLM model & updating it with new weights and vector embeddings to get better response, but this is very expensive and time consuming which not everyone has in this cloud day and agent we want to only optimize but also make it efficient on top of making it effective.

"LLM/SLM model" Large Language Model/ Small Language Model - are basically pretrained models on word data or some are focused and even modals that can be text , video, image, or both or all. they can be on prem or on cloud API based access..

"on prem or on cloud API " when we deploy a model that is downloaded from hugging face site or other trusted references we can increase privacy but at the cost of compute and cost that may work on private government containers, however we can get same level of security or similary using API based models that gurantees security of data and not only training from our data i.e. security at rest? but ensuring security at transit by not storing the data in memory like a vector database.

?"vector database" a vector database involves storing data in embeddings which is a multi dimensional representation?of text, like we had TF-IDF, word2vector etc... they simply help give semantic meaning numerically to text. so, for example, if we have? queen, aliens , water etc... and we add few dimensions to make this meaningful for AI it can be dimension 1 royalty which will be 1 for king and queen and zero for aliens and water. so, when we do cosine similarity for king it will have more similarities with queen than a alien. and as you can imagine adding more dimensions will only help to bring may be water more related to king and queen and not a alien we know that drinks only nitrogen hypothetically.

"cosine similarity" in a multidimensional spaces we have many ways to find distance between vectors, we can use Euclidean distance, or cosine?distance etc... to get percentage and use that to find the next word? or a token in particular which is what generative AI does because of which you see that text generation of one or two words at a time its simply generating it or finding cosine similarity and returning the closet so if we do API also or use platform like stream lit use streaming and i like it that way because the streaming is identical to how its actually generating data and is more relatable to the technology.?

"federated AI" so on top of generative AI that generates, RAG that sets the context now we need a closed room philosophy which only federated AI gives to make the actual data private and only share weights and quantized embeddings public ally that would not make sense publicly for PII but more importantly useful for the greater good. imagine a successful cancer research where we want to secure the individuals data that's personal but the word should benefit for the greater good.

"reduce carbon footprint" on top of the greater good by means of federated AI, we need to ensure that our prompts have the most context so it reduces the compute as much as possible so we reduce global warming and not only reused plastic, metals in the physical word but also prompts in the virtual word by having a prompt delivery system.