--- id: "/data-datasets/data-engineering-rag/page/2/" title: "data engineering & RAG: 52 curated resources · page 2" url: "https://skillissue.dev/data-datasets/data-engineering-rag/page/2/" --- # data engineering & RAG: 52 curated resources data engineering & RAG [Beginner](/data-datasets/beginner/index.md) [Intermediate](/data-datasets/intermediate/index.md) [Advanced](/data-datasets/advanced/index.md) [Expert](/data-datasets/expert/index.md) 52 resources · page 2 of 2 - ### [AI-Network-Troubleshooting-PoC (Cisco Code Exchange)](/context-engineering/retrieval-and-rag/network-routing-fault-diagnosis-with-telemetry-and-agents-ai-network-troubleshoo/index.md) by [Cisco](https://www.cisco.com/) Cisco Code Exchange entry for a proof-of-concept that uses AI agents and telemetry to troubleshoot network faults. Intermediate–Advanced learning_resource docs en - ### [RAG survey (Gao et al.)](/context-engineering/retrieval-and-rag/rag-survey-gao-et-al/index.md) by [Yunfan Gao et al. (Tongji University)](https://www.tongji.edu.cn) arXiv paper (2023) surveying retrieval-augmented generation for LLMs with a naive, advanced and modular RAG taxonomy. Intermediate learning_resource paper China en - ### [Ragas](/context-engineering/retrieval-and-rag/ragas/index.md) by [Ragas (Exploding Gradients)](https://www.ragas.io/) Ragas documentation: open-source framework with metrics for evaluating RAG and LLM applications. Intermediate learning_resource docs India en - ### [aider docs: Usage](/context-engineering/retrieval-and-rag/repository-aware-code-generation-and-editing-usage-aider-verified-local-models-s/index.md) by [Aider](https://aider.chat/) Aider usage documentation: how to add files, chat and edit code in a git repo, including with local models. Intermediate–Advanced learning_resource docs West en - ### [Selectel tutorial: Deploying a private LLM in a Kubernetes cluster](/context-engineering/retrieval-and-rag/selectel-private-llm-rag-in-managed-kubernetes/index.md) by [Selectel](https://selectel.ru/) Selectel tutorial in Russian on deploying a private LLM and RAG stack (vLLM, Qdrant, n8n) in Managed Kubernetes. Intermediate learning_resource guide Russia ru - ### [JetBrains AI Assistant: Generate documentation](/context-engineering/retrieval-and-rag/source-code-documentation-and-docstring-generation-generate-documentation-ai-ass/index.md) by [JetBrains](https://www.jetbrains.com/) JetBrains AI Assistant documentation on generating code documentation with AI and customising the prompt. Intermediate learning_resource guide CZ/EU en - ### [Get started with Copilot in Excel (Excel support)](/context-engineering/retrieval-and-rag/spreadsheet-formula-and-workbook-automation-get-started-with-copilot-in-excel/index.md) by [Microsoft](https://www.microsoft.com/) Microsoft Support getting-started guide for Copilot in Excel: data analysis, formulas and charts. Intermediate–Advanced learning_resource docs US en - ### [The Pile](/context-engineering/retrieval-and-rag/the-pile/index.md) by [Leo Gao et al. (EleutherAI)](https://www.eleuther.ai/) arXiv paper (2021) "The Pile: An 800GB Dataset of Diverse Text for Language Modeling", describing its 22 component sources and construction. Intermediate learning_resource paper US en - ### [Writing tests with GitHub Copilot](/context-engineering/retrieval-and-rag/unit-and-integration-test-generation-writing-tests-with-github-copilot-verified-/index.md) by [GitHub](https://github.com/) GitHub tutorial on generating unit and integration tests with GitHub Copilot. Intermediate learning_resource guide US en - ### [Alibi Detect](/context-engineering/retrieval-and-rag/alibi-detect/index.md) by [Seldon](https://www.seldon.io/) Alibi Detect documentation: Python library for outlier, adversarial and data-drift detection in ML systems. Advanced learning_resource docs UK/global en - ### [Cleanlab documentation](/context-engineering/retrieval-and-rag/cleanlab-documentation/index.md) by [Cleanlab](https://cleanlab.ai/) Cleanlab documentation: open-source library for finding label errors and data issues in ML datasets. Advanced learning_resource docs US en - ### [Datasheets for Datasets](/context-engineering/retrieval-and-rag/datasheets-for-datasets/index.md) by [Timnit Gebru et al. (Microsoft Research)](https://www.microsoft.com/en-us/research/) Microsoft Research page for Datasheets for Datasets, proposing standard documentation for ML datasets. Advanced learning_resource paper United States / international collaboration en - ### [LanceDB documentation](/context-engineering/retrieval-and-rag/lancedb-documentation/index.md) by [LanceDB](https://lancedb.com/) LanceDB documentation: multimodal lakehouse and vector database for AI data, with versioned tables and retrieval. Advanced learning_resource docs US en - ### [NeurIPS paper checklist](/context-engineering/retrieval-and-rag/neurips-paper-checklist/index.md) by [NeurIPS Foundation](https://neurips.cc/) NeurIPS paper checklist covering assumptions, limitations, experimental details, reproducibility and broader impacts. Advanced learning_resource guide Global en - ### [Qdrant documentation](/context-engineering/retrieval-and-rag/qdrant-documentation/index.md) by [Qdrant](https://qdrant.tech/) Qdrant documentation: vector search engine with quickstarts, filtering, hybrid search and deployment guides. Advanced learning_resource docs Germany en - ### [Retrieval-Augmented Generation](/context-engineering/retrieval-and-rag/retrieval-augmented-generation/index.md) by [Patrick Lewis et al. (Facebook AI Research)](https://ai.meta.com/) arXiv paper (2020) "Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks", introducing retrieval-augmented generation (RAG). Advanced learning_resource paper US; UK en - ### [Weaviate documentation](/context-engineering/retrieval-and-rag/weaviate-documentation/index.md) by [Weaviate](https://weaviate.io/) Weaviate documentation: open-source vector database with guides on indexing, search and RAG pipelines. Advanced learning_resource docs Netherlands en - ### [DataComp / DCLM](/context-engineering/retrieval-and-rag/datacomp-dclm/index.md) by [DataComp team (University of Washington and others)](https://www.datacomp.ai/) DataComp site: data-centric benchmarks for building better training datasets for CLIP models and language models (DCLM). Expert learning_resource docs US en - ### [DCLM paper](/context-engineering/retrieval-and-rag/dclm-paper/index.md) by [Jeffrey Li et al. (University of Washington)](https://www.washington.edu/) arXiv paper (2024) introducing DataComp-LM (DCLM), a testbed for curating language model training sets. Expert learning_resource paper US en - ### [Microsoft GraphRAG](/context-engineering/retrieval-and-rag/microsoft-graphrag/index.md) by [Microsoft](https://opensource.microsoft.com) Microsoft GraphRAG documentation: RAG approach that builds an LLM-generated knowledge graph for global summarisation queries. 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