Category: Organisational Security

Appropriate logging facilitates performance, cost and security analyses. We share three key categories of data and metadata every production RAG system should maintain within their logs.

Guardrails are key, but the diversity and complexity of input formats makes it challenging to put them in place, especially for multimodal systems.

Post-retrieval, a combination of filtering and reranking of search results help to refine, optimize and stack content for LLM operations.

The pre-retrieval phase is a critical stage where user input is processed and transformed into optimized vector search queries

The way content is chunked has a profound impact on the eventual performance of a RAG system. We introduce three broad approaches to chunking.

A well-designed data pipeline ensures that large volume of data is accurately and consistently incorporated. We outline 7 key design considerations for production-grade enterprise RAG data pipelines.

Different vector databases support different search algorithms, distance metrics, scalability, security capabilities, and options for performance optimization

In any given RAG system, the choice of model and precision is often a trade-off between performance and cost

API model access vs hosted model on self-managed instance: A key decision point for CTOs, CISOs and AI Engineers

“The case for production-grade RAG systems in enterprises warrant much deeper scrutiny over system design, given performance, cost and security considerations.”