Engineering a Sales-Domain Expert Foundation Model via Quantized Cross-Architecture Merging: Part III—Retrieval-Augmented Generation Integration and Knowledge Management

This paper continues the documentation of the twelve-phase pipeline for constructing a sales-domain super-expert foundation model, focusing on Phase 6: retrieval-augmented generation (RAG) integration and knowledge management. We report on the connection of the hierarchical sales knowledge processed in Part I (Phase 2) to the fine-tuned model produced in Part II (Phase 7), enabling the model to access current information and company-specific knowledge dynamically during inference. The work addresses how indexed chunks from the hierarchical dataset processing pipeline are queried at inference time, how retrieved passages are conditioned into the model context, and how retrieval quality and latency are balanced for enterprise sales workflows. We position Phase 6 within recent advances in retrieval-augmented generation [Lewis et al., 2021; Gao et al., 2024], dense retrieval [Karpukhin et al., 2020], and document segmentation for retrieval [e.g. semantic and hierarchical chunking], and we establish the technical basis for the agentic orchestration and production deployment documented in Parts IV and VI. Our contributions include a systematic integration of RAG with the sales-domain foundation model, design choices for retrieval granularity and context assembly that remain generic and reproducible, and engineering considerations that connect the training pipeline to downstream agentic and deployment stages.