CORE CONCEPTS

RAG (RETRIEVAL-AUGMENTED GENERATION)

RAG combines semantic search with language model generation. When a user asks a question, the system retrieves relevant chunks from your document library and provides them as context to the model — ensuring answers are grounded in your actual knowledge base.

This approach delivers accurate, source-cited responses without requiring the model to memorize your entire corpus.

LORA (LOW-RANK ADAPTATION)

LoRA is a fine-tuning technique that adds small, trainable adapter layers to a foundation model. Instead of modifying billions of parameters, LoRA encodes your domain-specific knowledge efficiently — at a fraction of the cost of full model training.

Your LoRA adapter is private, portable, and deletable at any time.

KNOWLEDGE INGESTION, TRAINING DATA & DEDICATED INFERENCE

Knowledge Ingestion is the process of uploading, extracting, chunking, and embedding your proprietary documents into a searchable index.

Training Data Generation automatically creates question-answer pairs from your documents, forming the structured dataset used to fine-tune your LoRA adapter.

Dedicated Inference means your model runs on isolated compute endpoints — no shared resources, no cross-tenant exposure, and consistent performance under load.