Enterprise RAG: Model Architecture & Prompt Design

The previous lesson produced a ranked set of candidate chunks through hybrid retrieval with learned fusion. Those chunks now sit at the boundary between retrieval and reasoning, and the design decisions you make from this point forward determine whether the system produces a faithful, grounded enterprise answer or a confidently wrong hallucination. This lesson picks up exactly at that boundary and architects everything that follows: reranking for precision, context assembly under token constraints, prompt construction with safety guardrails, and the generation stage itself.

The interview question you should be ready to answer is direct. Given a set of retrieved chunks, how do you design the reranking, context assembly, prompt construction, and generation stages to produce a safe, grounded response? The canonical enterprise RAG architecture follows a three-stage pipeline where retrieval feeds into reranking, which feeds into generation. Each stage optimizes for a distinct objective. Retrieval maximizes recall, reranking maximizes precision, and generation maximizes faithfulness. Production systems at companies like Microsoft and Google treat each stage as an independently tunable component with its own latency budget. This lesson also covers guardrails and agentic RAG for multi-hop questions, both of which are Staff+ interview differentiators.

The following diagram captures the full pipeline from query to verified response.

With the full architecture visible, the next step is to examine the reranking stage that bridges high-recall retrieval and precise context assembly.

Reranking for precision

The retrieval stage intentionally casts a wide net, returning 20 to 50 candidate chunks to maximize recall. But the context window of the downstream LLM can only accommodate 5 to 10 of those chunks. The reranker’s job is to convert that broad candidate set into a precision-ranked shortlist.

How cross-encoder reranking works

A cross-encoderA transformer model that takes two text inputs concatenated as a single sequence and produces a joint relevance score, capturing fine-grained token-level interactions that independent encodings miss. scores each query-chunk pair by processing them together through a transformer such as a fine-tuned BERT variant or a model like bge-reranker. This joint encoding captures semantic interactions between the query and the chunk that bi-encoder retrieval cannot detect, because bi-encoders encode the query and document independently into separate vectors before ...