name:rag-systems

description:Build retrieval-augmented generation pipelines grounded in enterprise knowledge. Use when an LLM must answer over a private, changing corpus (internal docs, product data, regulated content) and hallucination is unacceptable.

RAG Systems

An LLM over your own data is only useful if it stops guessing. Grounding and evaluation are the whole job; generation is the easy part.

Approach

Chunk with structure in mind. Respect sections, tables, and headings when splitting. Naive fixed-size chunks destroy the context that makes retrieval work.
Retrieve, then rerank. Combine dense and keyword retrieval for recall, then rerank with a cross-encoder for precision. They are two different problems; do both.
Ground and cite. Constrain generation to the retrieved context and attach citations so any claim can be traced back to a source.
Correct when retrieval is weak. Grade the retrieved context; if it is thin, rewrite the query and retry (Corrective RAG) instead of answering from nothing.
Evaluate for real. Track faithfulness and answer relevance against an eval set. "Looks good to me" is not a metric.

Defaults

Retrieval quality caps answer quality — spend your time there before touching the prompt.
A confident wrong answer is worse than "I don't know". Prefer abstention on weak context.

Evidence

A conversational analytics agent on Snowflake Cortex letting management query enterprise data in natural language.
Vector-based research agents with semantic deduplication in an autonomous content pipeline.

Stack

Chroma · Pinecone · Snowflake Cortex · LangChain · Python