What is RAG, Actually?#
- Retrieval-Augmented Generation (RAG) is the process of optimizing the output of a large language model, so it references an authoritative knowledge base outside of its training data sources before generating a response.
- Large Language Models (LLMs) are trained on vast volumes of data and use billions of parameters to generate original output for tasks like answering questions, translating languages, and completing sentences.
- RAG extends the already powerful capabilities of LLMs to specific domains or an organization’s internal knowledqe base, all without the need to retrain the model. It is a cost-effective approach to improving LLM output so it remains relevant, accurate, and useful in various contexts.
CONS of only using the LLMs#
- llms are trained on a specific set of data, suppose knowledge cut off is month ago, so for current world data it will hallucinate.
- also it cannot access private or internal data. such as hr policies, finance docs etc
- fine tuning is a very expensive and tedious process cuz it involves tweaking billions of parameters.
Data Ingestion Pipeline#
Similarity search#
- technique used within the RAG pipeline to find info relevant to user query from a db.
- to perform - text data is converted to vectors using embedding models
- the user query is also converted to vector, the system applies algo like cosine similarity to compare query against the vector in the vector db.
- the search identifies the
chunks of datamost similar to req, this retrieved info is calledcontext, which is then sent to LLM to help it generate an accurate answer.
Semantic search#
is an advanced search method that goes beyond keyword matching to understand the user’s intent and the contextual meaning of a query.
