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In this article, we will show you how to use Haystack to evaluate the performance of a RAG pipeline. Note that the code in this article is meant to be illustrative and may not run as is; if you want to run the code, please refer to the python script.
When building real-world applications based on Language Models (such as RAG), evaluation plays an important role. Recently, evaluating generated answers using powerful proprietary Language Models (such as GPT-4) has become popular and correlates well with human judgment, but it comes with its own…
Retrieval-augmented generation (RAG) systems combine generative AI with information retrieval for contextualized answer generation. Building reliable and performant RAG applications at scale is challenging.
Getting your LLM application into production is a huge milestone, but that’s only the beginning. It’s critical to monitor how your pipeline is performing in the real world so you can keep improving performance and cost, and proactively address any issues that might arise.
This is part one of the Advanced Use Cases series: 1️⃣ Extract Metadata from Queries to Improve Retrieval 2️⃣ Query Expansion 3️⃣ Query Decomposition 4️⃣ Automated Metadata Enrichment In Retrieval-Augmented Generation (RAG) applications, the retrieval step, which provides relevant context to your…
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