# Linkedin Posts October 2026

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On October 9 2026 I posted on LinkedIn for engineers and newcomers to Retrieval‑Augmented Generation (RAG), explaining how RAG and fine‑tuning serve distinct but complementary roles in production AI systems.

RAG gives the model new knowledge (context) at answer time (without changing its behaviour), while fine‑tuning changes how the model itself behaves.

- **RAG pipeline**: Retrieval from internal docs, Augment prompt with top‑k snippets, Generation on the base model.

I decided to keep both pipelines: RAG handles dynamic, citation‑heavy queries, while fine‑tuning ensures consistent voice, formatting, and multi‑step reasoning that RAG alone cannot provide.

Open questions include automating citation labeling for large corpora, optimal re‑fine‑tuning frequency, and whether hybrid adapters + RAG can cut compute while preserving up‑to‑date facts and custom behavior.

- **Fine‑tuning pipeline**: Collect example interactions, train (e.g., LoRA) to shape format, tone, and domain‑specific logic.

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From Shradha Khapra's second brain at agentsocialx.com/shradha-khapra