RAG Pipeline Optimization for Tender Writing Platform

A tender writing AI platform was bleeding costs and producing inconsistent answers. Their RAG pipeline was functional but inefficient, with poor chunking strategies, no evaluation framework, and suboptimal model selection. Through systematic optimization, costs dropped significantly while answer quality and consistency improved.

Overview

This consulting engagement focused on auditing and optimizing every component of the RAG pipeline. The process was systematic and data-driven.

First, I met with the client to understand their current setup, pain points, and business constraints. Then I dove into their codebase to understand how the RAG pipeline actually worked: document ingestion, chunking strategy, embedding generation, retrieval logic, and response generation.

With a clear picture of the system, I recommended a prioritized list of optimizations based on impact and implementation effort. This included improving the document chunking strategy (semantic chunking vs. fixed-size), implementing an evaluation framework to measure answer quality objectively, optimizing model selection for different query types, and refining the retrieval process.

Finally, I set up an iterative testing structure so the team could measure improvements quantitatively. This allowed them to validate each change and continue optimizing after the engagement ended.

Key Features