Results for “rag-evaluation”
11 skills
alirezarezvani RAG Architect
Design, tune, and evaluate production RAG pipelines with deterministic tools for chunking, pipeline design, and retrieval evaluation.
20.4k · bundle
nvidia RAG Eval
Evaluates RAG pipelines using a filesystem-based benchmark with corpus/ and train.json, running evaluate_rag.py to tune retrieval and generation flags and interpret RAGAS metrics.
2.2k · bundle
oyi77 RAG Builder
Designs and implements RAG pipelines, covering document chunking, embedding strategies, hybrid search, answer synthesis with source attribution, and evaluation using RAGAS metrics.
10
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jeffallan RAG Architect
Designs and implements production-grade RAG systems by chunking documents, generating embeddings, configuring vector stores, building hybrid search pipelines, applying reranking, and evaluating retrieval quality.
10.4k · bundle
nvidia RAG Perf
Run config-driven performance benchmarks against a deployed NVIDIA RAG Blueprint server, including profiling and load testing, with a unified report.
2.2k · bundle
majiayu000 RAG
Builds Retrieval-Augmented Generation systems with document chunking, embedding generation, vector storage, and retrieval pipelines, including evaluation and optimization.
567 · bundle
joshuashepherd Build RAG
Builds or modifies a RAG pipeline with intent-based routing, vector store search, citation rendering, and book fidelity enforcement.
1
mukul975 Testing Prompt Injection In RAG Pipelines
Probe RAG applications for prompt injection via poisoned retrieved context and embedding manipulation.
24.6k · bundle
joshuashepherd Agent RAG
Build or modify a RAG retrieval pipeline with vector store search, corpus routing, citation rendering, and book fidelity enforcement.
1
lucaspmarie-a11y LLM Ops
Provides guidance on production AI operations including RAG pipelines, vector databases, embeddings, fine-tuning, prompt engineering, cost estimation, and quality evaluation.
5
antigravity LLM Ops
Provides guidance and code for production AI workflows including RAG pipelines, vector databases, embedding indexing, prompt engineering, cost estimation, semantic caching, and quality evaluation.
42.4k