Packs
3 packscurated
Build RAG Pipeline with Pinecone
Build a production RAG pipeline and persistent agent memory using Pinecone as the vector database backend.
6 skills · pack
@dotnet
Dotnet AI
AI and ML skills for .NET: technology selection, LLM integration, agentic workflows, RAG pipelines, MCP, and classic ML with ML.NET.
5 skills · pack
@alirezarezvani
Engineering
37 advanced engineering skills: agent designer, agent workflow designer, RAG architect, database designer + schema designer + SQL assistant, migration architect, observability designer, dependency auditor, changelog generator (with semantic version bumper and hotfix/rollback procedures), API design reviewer, API test suite builder, CI/CD pipeline builder, MCP server builder, skill security auditor
33 skills · pack
Results for “rag-pipeline”
9 skills
nimoqup046-collab ai-ml
Orchestrates AI/ML development workflows covering LLM applications, RAG systems, AI agents, ML pipelines, and observability.
2
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
lingxling ai-ml
Orchestrates AI/ML workflows for building LLM applications, RAG systems, AI agents, and ML pipelines, covering design, integration, observability, and security.
253
More results
jorcan ai-ml
Orchestrates AI/ML workflows for building LLM applications, RAG systems, AI agents, and ML pipelines, covering design, integration, observability, and security.
0 · bundle
phoroth ai-ml
Orchestrates AI/ML workflows for building LLM applications, RAG systems, AI agents, and ML pipelines, covering design, integration, observability, and security.
3
jeffallan ml-pipeline
Designs and implements production-grade ML pipeline infrastructure: configures experiment tracking, creates orchestration DAGs, builds feature store schemas, deploys model registries, and automates retraining and validation workflows.
10.4k · bundle
lucaspmarie-a11y ai-ml
Orchestrates AI/ML workflows for building LLM applications, RAG systems, AI agents, and ML pipelines, covering design, integration, observability, and security.
5
jorcan llm-ops
Guides production LLM operations: RAG pipelines, embeddings, vector databases, fine-tuning, prompt engineering, cost estimation, quality evals, and AI architectures.
0 · bundle
phoroth llm-ops
Implements production LLM operations: RAG pipelines, embeddings, vector databases, fine-tuning, advanced prompt engineering, cost estimation, quality evals, semantic caching, streaming, and agents.
3