VEL ORENDISVel Orendis

Build an evaluated RAG assistant

A grounded assistant with measured retrieval, judged answers and live monitoring.

24 lessons, about 4.2 h. Anything you already know, you can prove and skip.

0 of 24 lessonsquests defended, 1 open now

Start with What the Model Cannot Know
  1. The request path and where time goesFoundations, 10 min
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  2. Tables, keys and modellingDatabases, 10 min
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  3. Observability: logs, metrics, tracesReliability, 12 min
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  4. What the Model Cannot KnowWorking with LLMs, 8 min
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  5. Calling a model: messages, tokens and costWorking with LLMs, 10 min
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  6. Prompts that ground answersWorking with LLMs, 10 min
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  7. Structured output and validationWorking with LLMs, 10 min
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  8. The RAG pipelineSearch and RAG, 10 min
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  9. Lexical search and BM25Search and RAG, 12 min
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  10. Embeddings and similaritySearch and RAG, 12 min
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  11. Vector search, exact and approximateSearch and RAG, 12 min
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  12. Chunking long documentsSearch and RAG, 12 min
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  13. Ground truth for searchRetrieval quality, 10 min
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  14. Hit rate, MRR and other metricsRetrieval quality, 12 min
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  15. Hybrid search and rank fusionRetrieval quality, 12 min
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  16. Query rewriting, thresholds and the honest no-answerRetrieval quality, 10 min
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  17. Offline and online evaluationEvaluation and monitoring, 8 min
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  18. LLM as a judgeEvaluation and monitoring, 12 min
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  19. Logging every LLM callEvaluation and monitoring, 10 min
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  20. Feedback and online judgesEvaluation and monitoring, 10 min
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  21. Dashboards and traces for LLM appsEvaluation and monitoring, 12 min
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  22. Testing strategyShipping, 10 min
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  23. Configuration, secrets and containersShipping, 10 min
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  24. From notebook to serviceShipping, 10 min
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