ai-engineer

To build a RAG pipeline with FAISS-based vector search, you need to generate embeddings from your knowledge base and use FAISS to retrieve relevant context for your LLM. This handles embedding generation, knowledge base construction, and prompt engineering.

LLM integration and model routing coordinate requests between local and cloud language models to optimize performance and cost. The system applies prompt templates and routes queries to the appropriate model deployment based on configuration.

Implementing deterministic prompts and security guardrails requires applying strict prompt templates, caching responses, and enforcing error handling rules. This ensures your AI backend produces predictable outputs while maintaining secure data handling across LLM interactions.

Yes, you can use FAISS vector search with local LLMs to query a knowledge base. The system supports local and cloud LLM usage, allowing you to route queries to local models while using FAISS for retrieval.

The best way to evaluate and monitor RAG pipelines is to implement systematic evaluation across embedding generation, prompt engineering, and model coordination. This tracks retrieval accuracy and response quality while maintaining secure data handling.