RAG Pipelines

RAG Pipelines are systems that integrate large language models (LLMs) with external vector databases to enhance the accuracy and relevance of generated responses by first retrieving contextually relevant documents.

Improving the context-awareness and relevance of responses generated by large language models in enterprise settings where specific domain knowledge is crucial.

The process starts with a user query, which is then used to retrieve relevant document chunks from an external vector database based on cosine similarity. The retrieved documents are passed to the LLM for generating a response that incorporates this additional contextual information, thereby improving the quality and accuracy of the output.

• Large language models
• Vector databases
• Cosine similarity

N/A

Developing RAG Pipelines involves integrating an LLM with a vector database, fine-tuning the model for specific use cases, and optimizing the retrieval mechanism to ensure efficient and accurate document fetching.

Curated names only — none are invented. Use the link to find more.

Cost drivers only — no verified dollar figures are shown. Check live sources for prices.

Illustrative — search real, dated examples rather than trusting a generated story.

Live searches — we don't list papers we can't verify.

Live patent searches — filings are never listed from memory.

LLM

Large Language Model

vector database

A type of database that stores and retrieves data in vector form, often used for efficient similarity searches.

cosine similarity

A measure of similarity between two non-zero vectors of an inner product space. In the context of RAG Pipelines, it is used to find relevant document chunks based on textual similarity.

Verify against primary sources only.

Source: curated technology intelligence stream with tracked references.