Why Building Production-Grade RAG Applications Is So Hard
Learn why creating demo RAG applications is easy, but building production-grade systems is exponentially harder, and how Queryloop solves these challenges.
Creating a demo for Retrieval Augmented Generation (RAG) is easy, but building a production-grade app is 10x harder—if not more. For every blog or tutorial claiming you can launch a RAG app in under an hour, there are hundreds discussing the complexities of building LLM and RAG systems that reliably deliver acceptable accuracy, latency, and cost.
The Challenge of Building Production-Grade RAG
- Which parser works best for a PDF containing both text and tables?
- What is the right chunk size for my use case?
- Which embedding model suits my needs?
- What retrieval method should I use?
- How many search results should I retrieve?
- Should I use a reranker?
- Which LLM fits my use case?
Real-World RAG Optimization Challenges
We see these challenges in practice. OpenAI, on their Devday, explained that while building a RAG pipeline for an enterprise client, they started with a baseline accuracy of 45%. They then experimented—through a long process of trial and error—with techniques like Hypothetical Document Embeddings, fine-tuning embeddings, and adjusting chunk sizes.
Nvidia recently noted that there are 15 different control points in a RAG pipeline, each impacting the final result. Factors such as query rewriting strategy, chunk size, pre-processing technique, metadata enrichment, reranking, and LLM selection all matter.
Queryloop's Automated Solution

Two Core Optimization Tools
- A Retrieval Tool that optimizes how context is extracted
- A Generation Tool that fine-tunes the LLM output
Comprehensive Evaluation Dashboard
For evaluation of each combination, we have built our own evaluation methods that improve upon open source approaches such as RAGAS.
Success Stories
Customers like Guidelinebuddy have already experienced our seamless app building workflow and successfully deployed optimal RAG applications with Queryloop.