How to Evaluate a RAG Solution Before Deploying it in Your Organisation
This white paper outlines the essentials for deploying and optimizing a RAG solution, with practical methods, use cases, and evaluation tools.
Deploying a RAG (Retrieval-Augmented Generation) solution in an enterprise environment is not something you can wing. Before going live, it is essential to validate that the system is capable of returning relevant responses aligned with users’ actual intentions. This is precisely what RAG evaluation is about — a step that is all too often overlooked, yet decisive for the success of the project.
What is a RAG solution?
RAG is an approach that combines a document search engine with a language model (LLM). Unlike a standalone LLM, a RAG system can query internal databases, business documents or up-to-date sources to enrich its responses — making it particularly well suited to enterprise use cases where accuracy and reliability are critical.
Why evaluate before deploying?
Without structured evaluation, a RAG solution may appear to work well in a demonstration, yet fail in production when faced with real-world questions. The most common issues fall into four categories: a retrieval problem (the right documents are not surfaced), a data problem (the knowledge base is incomplete), a generation problem (the LLM does not make proper use of the context provided), or a usage problem (users do not know how to query the system effectively).
Coexya’s 3-step evaluation methodology
The methodology developed by Coexya’s Search & Semantics team is built around three steps. First, the creation of a gold standard: a reference framework pairing sample questions with expected answers, which serves as an objective benchmark for comparison. Second, the classification of generated responses on a 4-level relevance scale, ranging from “off-topic” to “complete”. Third, gap analysis to identify the precise root cause of each unsatisfactory response and prioritise the adjustments required.
Automated evaluation: a lever for efficiency
For complex RAG systems covering a broad functional scope, manual evaluation quickly becomes costly and difficult to reproduce consistently. Specialist frameworks such as RAGAS or LangSmith make it possible to automate this process using the LLM-as-a-judge principle — a secondary language model assesses the quality of generated responses against measurable criteria: faithfulness, response relevancy, context precision, and noise sensitivity.
Coexya’s expertise
With over 20 years of experience in information retrieval solutions and unstructured data processing, Coexya’s Search & Semantics team supports its clients from initial scoping through to production deployment and ongoing maintenance of RAG solutions, embedding a rigorous and continuous evaluation approach from the outset.
Want to go further? Download our full white paper to discover our detailed methodology, key metrics (Faithfulness, Response Relevancy, Context Recall…) and practical recommendations for optimising your RAG solution.
