“RAG”

Retrieve relevant excerpts from a project's text files without loading entire files into context.

Set up RAG / semantic search with laravel/ai — a vector column + embeddings ingestion + similarity search (and optional reranking).

Build retrieval and RAG with Anvia — chunking, embeddings, vector stores, knowledge graphs, filters, and search tools.

Designs and implements production-grade RAG systems by chunking documents, generating embeddings, configuring vector stores, building hybrid search pipelines…

Use for RAG pipelines, retrieval augmented generation, ColBERTv2, context retrieval, multi-hop RAG, and grounded DSPy answers.

Build a local RAG index by splitting a document and embedding its chunks using the on-device engine (private/offline).

Use when building self-correcting retrieval systems for AI agents.

Manage and query Agent Platform RAG Engine Corpora and retrieve grounded contexts using the Google GenAI SDK.

Query historical test runs, failures, and API specs from the SQLite RAG index via MCP.

Use when needing global or relational understanding of codebases or knowledge corpora.

RAG 模式 —— 检索增强生成架构设计、分块策略、向量检索优化与评估方法

Use quando precisar buscar contexto vetorial, gerar embeddings, ou consultar o RAG.

RAG pipeline design — chunking, embeddings, retrieval strategies, evaluation, and demo patterns.

Discovers requirements, and generates architectural, design, and deployment guidance for a retrieval-augmented generation (RAG)-capable enterprise search…

RAG (Retrieval-Augmented Generation) system for affidavit workflows.

Detect persistent-memory poisoning and RAG/knowledge-base contamination via dialogue testing.

Use for RAG, vector search, semantic search, embeddings, knowledge bases, long-term agent memory, and grounding answers in the project's own documentation.

Runs checklists and workflows for designing, layering, evaluating, and operating production RAG and AI search — hybrid retrieval, agentic context tools…

对话时的角色 system prompt（问答与选定文章摘要共用；引用硬性规则由系统在问答时自动追加）

Runs workflows for AIEWF 2024 RAG talks — layout-aware ingest, GraphRAG, eval-driven tuning, Pydantic structured IO, MongoDB vectors, EMT memory, construction…

Acts as the verification and grounding layer in Retrieval-Augmented Generation (RAG) pipelines.

INVOKE THIS SKILL when building ANY retrieval-augmented generation (RAG) system.

AIChat is a comprehensive LLM command-line tool written in Rust that combines chat-REPL, shell command generation, RAG, AI tools, and multi-provider support…

Private knowledge base for indexing multimodal files or folders into a knowledge graph, supporting multi-hop graph retrieval