RAG Systems · Engineering Notes · Part 1 of 2
Building a Production-Ready RAG System
From retrieval to reliable answers: engineering every stage of the pipeline, and the open questions we set out to solve next.
RAG Systems · Engineering Notes · Part 1 of 2
From retrieval to reliable answers: engineering every stage of the pipeline, and the open questions we set out to solve next.
This is where we started. See how the system evolved into a fully tested, production-hardened RAG platform.
Read Part 2Building a production-grade Retrieval-Augmented Generation (RAG) system is not just about connecting a vector database to an LLM.
It's about carefully engineering every stage of the pipeline to ensure accuracy, relevance, and trustworthiness at scale.
Over the past few weeks, we've been deep in the process of designing, testing, and refining an end-to-end RAG pipeline. This post outlines the architecture, key design decisions, and areas of ongoing exploration.
At a high level, the system is designed to:
"The focus throughout has been clear: give the LLM the right information, not just more information."
Seven pieces of the pipeline, from retrieval through to answer generation, each solving a distinct part of the accuracy problem.
Instead of relying on a single retrieval method, the system combines BM25 (keyword-based search) for exact term matching with vector embeddings for semantic similarity. This hybrid approach ensures both lexical precision and contextual understanding, significantly improving recall across diverse query types.
To merge results from multiple retrieval strategies, RRF combines rankings in a way that consistently surfaces the most relevant documents across methods, rather than depending on raw scores which are often not directly comparable.
After initial retrieval, results are passed through a cross-encoder reranker for stronger semantic relevance and better ordering of top results. This step significantly improves answer quality for nuanced or ambiguous queries.
Context is constructed with token limits in mind, giving priority to high-relevance chunks and filtering out redundant or low-value content, ensuring optimal utilization of the model's context window.
Carefully designed prompts ground the model strictly in retrieved context, reduce hallucinations, and encourage structured, explainable responses.
For low-confidence scenarios, the system avoids overconfident or misleading answers and falls back to safer responses. This is critical for building trust in real-world applications.
Each response includes references to source documents and a confidence score based on retrieval and reranking signals, adding transparency and allowing users to verify outputs.
"The quality of a RAG system is largely determined before the LLM is even called."
Improving retrieval precision and context construction has a far greater impact than tweaking prompts alone.
Finding the right trade-off between summarization (more coverage, risk of information loss) and truncation (exact data, limited context) is key for maximizing signal within token limits.
Building a production-ready RAG system is an iterative process that requires careful attention to retrieval, ranking, and context design, not just model selection.
As this system evolves, the goal remains the same: deliver answers that are not only fluent, but factual, grounded, and trustworthy.
Every item on the roadmap above became a real fix, measured through a purpose-built testing framework we hadn't built yet when this was written. Part 2 covers what we found: fragmented chunks, a testing tool that was silently marking correct answers wrong, and the security and rollback discipline needed before wider rollout.
Read Part 2: How We Engineer AI Assistants That Employees Actually Trust Back to Blog
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