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The Engineer’s Framework for LLM & RAG Evaluation
- The LLM Twin free course teaches how to design, train, and deploy a production-ready LLM twin of yourself powered by LLMs, vector DBs, and LLMOps good practices, and build and deploy your LLM Twin.
- The most efficient way when building AI apps, before optimizing anything, is to create an end-to-end flow of your feature, training, and inference pipelines and spend some serious time on your evaluation pipeline.
- Usually, heuristic metrics don’t work well when assessing GenAI systems as they measure exact matches between the generated output and GT. Therefore, LLM systems are primarily evaluated with similarity scores and LLM judges.
- The Opik framework is used to train, evaluate and compare multiple LLM experiments by quantizing the results of your experiments, including metadata such as the version of artifacts used to compute the dataset, the embedding model, and more.
- With RAG, you have an extra dimension that we have to check, which is the retrieved context. Thus, there are 4 dimensions to evaluate, and NDCG measures are used during the retrieval step, and similar strategies are leveraged during the generation step.
- Opik is used to compute metrics relevant to RAG, which track the embedding model used at the retrieval step in our experiment metadata and uses the ContextRecall and ContextPrecision metrics that use LLM judges to score the quality of the generated answers.
- By leveraging the Opik platform, you can quantify and optimize your LLM and RAG experiments by measuring various strategies and choosing the best one.
- Optimization of LLM & RAG evaluation pipelines can be done by computing predictions in batch instead of leveraging the AWS SageMaker inference endpoint, which can handle one request at a time.
- Ultimately, the course teaches how to evaluate LLM and RAG systems, enabling the creation of optimal AI applications.
- The LLM Engineer's Handbook is available to buy on Amazon or Packt.
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