Retrieval Supplies a Possible Correction

An embedding model maps the query and documents into a space where relevant passages can surface. The generator then receives the query, current answer, and new passage.

The critical operation lies in relevance judgment. Useful context changes or completes the answer; irrelevant context should not trigger invention, commentary, or needless paraphrase.

Make the Output Stand on Its Own

The instruction forbids references to the old answer or retrieval process. Readers receive the best final answer directly, with no editorial residue.

Teams building RAG systems can test this rewrite-or-repeat contract across adversarial, redundant, and corrective passages. The simplicity makes behavior measurable and gives production systems a clear fallback.

Put Retrieval Discipline Beside a Complete Training Run

The four-layer run exposes how a small language model is trained, giving the RAG argument a concrete base model and execution path.

Four-Layer Tiny Transformer Training Run · https://lnkd.in/g6CGSTuH