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Why Building Production-Grade RAG Applications Is So Hard
Queryloop Team
March 3, 2025
4 min read
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
Why is this so hard? Imagine you want to build a production-grade RAG pipeline. First, you need an optimal data parsing and ingestion system. Next, you must embed your parsed data with the right embedding model(s). Then, you have to choose an appropriate retrieval method and the right LLM—with an optimal prompt—to generate the desired response.
Every decision affects performance. Questions arise like:
- 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?
A naive configuration might work for simple queries over short documents, but it won't suffice for complex ones. With too many parameters to tune by hand—and the need for reliable evaluation methods—the system becomes incredibly hard to optimize.
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.
After about 20 iterations, they had only reached 65% accuracy. They further tried using cross‑encoders to re-rank search results, enriching context with metadata, refining prompt engineering, integrating a SQL database, and employing Query Expansion. Persisting with the methods that pushed accuracy upward, they eventually achieved 98% accuracy.
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
We built Queryloop out of frustration with the endless manual tuning of RAG apps. Our no‑code platform automatically explores a wide range of configurations for optimal retrieval and response generation, including adjusting chunk sizes, embedding models, rerankers, top‑k settings, chunking strategies, and LLM choice/prompts.
Rather than using a one‑size‑fits‑all approach, we tailor our search and response settings to match the unique needs of each document in your dataset.
Two Core Optimization Tools
For RAG optimization, Queryloop offers two core tools:
Together, these tools allow you to balance accuracy, latency, and cost effectively. Moreover, the platform can fine‑tune both embedding models and open source or proprietary LLMs to capture the nuances in context and language specific to your domain.
- A Retrieval Tool that optimizes how context is extracted
- A Generation Tool that fine-tunes the LLM output
Comprehensive Evaluation Dashboard
With Queryloop's experiments dashboard, you can evaluate each configuration across accuracy, latency, and cost, then deploy the best-performing setup with one click to immediately access a ready-to-use API.
For evaluation of each combination, we have built our own evaluation methods that improve upon open source approaches such as RAGAS.
The best part is that we continuously integrate the latest retrieval and generation strategies based on our own and public benchmarks, ensuring that your RAG or LLM app remains optimal. And soon, we'll roll out agentic workflow optimization to support multi-step reasoning, ensuring even the most complex use cases perform at their best.
Success Stories
Customers like Guidelinebuddy have already experienced our seamless app building workflow and successfully deployed optimal RAG applications with Queryloop.
We're excited to offer a free trial to new users starting today—give it a try and let us know what you think!
RAGLLMOptimizationProductionAI ApplicationsQueryloop
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