Bifalgorithm post

"This is RAC, this is Agent-A-RAC. What is the difference? RAC searches for relevant information and gives it directly to LLM to answer. Whereas Agent-A-RAC can decide what to search, which tools to use, search multiple times and even adapt the query if the first result is not good. This is a vector database, this is a graph database. What is the difference? A vector database finds information with a similar meaning, even when different words are used. A graph database stores connections between things such as which customer owns which account or which person works for which company. Basically, a vector database finds similarities and a graph database follows relationships. This is an ontology, this is a knowledge graph. What is the difference? An ontology defines the types of things that exist and how they can be connected. Knowledge Graph stores the actual things and their connections. For example, an Ontology says that a person can work for a company. A Knowledge Graph says that Sara works for Amazon. Basically, the Ontology defines a structure and the Knowledge Graph fills it with real facts. This is CAG, this is RAG. What is the difference? CAG prepares and caches the knowledge in advance so the model can reuse it when answering questions. RAC searches an external knowledge source whenever relevant information is needed. This is full fine-tuning, this is LoRa. What is the difference? Fine-tuning updates most or all of the model's weights, whereas LoRa keeps the original model mostly unchanged and trains only a small set of additional weights. This is quantization and this is knowledge distillation. What is the difference? Quantization makes the same model smaller by storing its numbers using fewer bits. Knowledge distillation trains a new smaller model to copy the behavior of a larger model. I hope this was helpful."

"This is RAC, this is Agent-A-RAC. What is the difference? RAC searches for relevant information and gives it directly to LLM to answer. Whereas Agent-A-RAC ca…