What Are the Best AI Skills for “RAG”?
18 skills tagged “RAG”, each forged from a YouTube creator's methodology.
Owain Lewis RAG Retrieval Strategy Selector
Given any AI data-retrieval problem, select and implement the correct RAG strategy from six production-tested approaches so the LLM gets exactly the right information to answer any question.
28 Aug 2026Sujan AnandSujan Anand RAG Application Build Framework
Build a fully working Retrieval Augmented Generation (RAG) system that answers questions from your own documents accurately, with zero hallucinations, by grounding every AI response in retrieved context.
28 Aug 2026CreateBytesCreateBytes Agentic Systems Build Framework
Design, architect, and deploy production-ready AI agent systems by correctly classifying the problem, selecting the right components, and applying cost-conscious multi-agent orchestration.
28 Aug 2026Zen van RielZen van Riel Local AI Fine-Tuning Pipeline
Apply a structured, five-step fine-tuning pipeline to transform any open-source base model into a task-specific or persona-specific AI that behaves in ways no prompt or RAG system can reliably replicate.
22 Aug 2026Tejas AITejas AI Agentic AI Builder Framework
Design, build, and deploy production-grade AI agent systems by applying the complete Agentic AI methodology — from core loop architecture through RAG, vector memory, multi-agent topologies, and safety guardrails.
21 Aug 2026edureka!Edureka MCP-RAG Agentic AI Build Framework
Build and deploy a production-ready RAG AI agent with multi-step reasoning, vector knowledge retrieval, tool-calling, and MCP hosting — without retraining any model.
21 Aug 2026Krish NaikKrish Naik Agentic AI Stack Builder
Build production-ready agentic AI applications using LangChain v1, LangGraph, RAG, Guardrails, and Evals by following a structured, layer-by-layer implementation methodology.
15 Aug 2026IntellipaatIntellipaat 2026 AI Engineering Roadmap
Apply a sequenced, build-first roadmap to go from zero AI knowledge to a deployable, hire-ready AI engineering skillset without wasting months on outdated theory-first approaches.
14 Aug 2026IntellipaatIntellipaat Agentic AI Systems Builder
Given any AI use-case scenario, apply a production-grade methodology to decide architecture, select the right LLM deployment model, build agentic workflows with LangChain/LangGraph, and implement RAG with guardrails — the way a working AI engineer would.
1 Aug 2026KodeKloudKodeKloud Complete RAG System Design Skill
Design, build, evaluate, and extend a production-ready Retrieval Augmented Generation (RAG) pipeline for any knowledge-base use case, using the right chunking strategy, vector database setup, retrieval metrics, and advanced RAG variant for the scenario.
31 July 2026IBM TechnologyIBM LLM Customization Stack Framework
Given any AI deployment scenario, determine the optimal customization path — from prompt engineering through RAG, agent skills, and fine-tuning — without wasting resources on training that frontier models may leapfrog overnight.
24 July 2026Tejas AITejas AI 5-Phase 2026 AI Blueprint
Transform from an AI beginner or plateau-stuck learner into a deployable 2026 AI practitioner who can architect, build, secure, and prove agentic AI systems that orchestrate intelligence and get paid.
18 July 2026T3chFestDenisov Agent Specialization RAG vs Fine-Tuning Framework
Given any AI agent use case, correctly decide whether to use RAG, fine-tuning, or a hybrid architecture — and implement the chosen approach without common data and retrieval mistakes.
18 July 2026DataTalksClub ⬛Grigorev RAG Application Build Framework
Build a working Retrieval Augmented Generation (RAG) application from scratch that answers domain-specific questions using a private knowledge base — without retraining any LLM.
17 July 2026Cole MedinCole Medin RAG 2.0 Agentic Knowledge System
Build a knowledge retrieval system where an AI agent reasons about whether to query a vector database, a knowledge graph, or both — delivering far more accurate and relational answers than naive RAG alone.
3 July 2026IBM TechnologyIBM CoALA Four-Type Agent Memory Framework
Design the right memory architecture for any AI agent by selecting and configuring the correct combination of the four CoALA memory types — working, semantic, procedural, and episodic — matched to the agent's complexity and purpose.
6 June 2026Unblocked Context Engine Framework
Stop babysitting your AI agents by building a context engine that gives them the organizational understanding they need to produce senior-engineer-quality, mergeable code without constant human correction.
29 May 2026AI EngineerWalsenuk Stop Babysitting Agents Framework
Design a Context Engine for your AI agents so they produce senior-engineer-approved output autonomously, without you pointing files and correcting mistakes in a doom loop.
26 May 2026