What Are the Best AI Skills for “LLM”?
18 skills tagged “LLM”, 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 2026Nick SaraevSaraev AI Agent Orchestration System
Deploy multi-agent architectures that parallelize work across specialized AI models, self-correct over time, and produce higher-quality outputs than any single-model approach.
22 Aug 2026Mehul MohanMehul Mohan AI Agent Build & Sell Framework
Design, deploy, and optionally monetise a reliable AI agent that connects multiple tools, compresses information, and delivers intelligent outputs on a schedule — without building the orchestration infrastructure from scratch.
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 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 2026Aishwarya SrinivasanAishwarya Srinivasan LLM Fine-Tuning Decision Framework
Choose the right fine-tuning methodology for any LLM use case and understand exactly what is happening under the hood — so you are not just making API calls but engineering with genuine depth.
14 Aug 2026KodeKloudKodeKloud LLM Fine-Tuning Pipeline
Transform a generic base LLM into a jailbreak-resistant, domain-specific agent by embedding behavior directly into model weights using LoRA and DPO — without a data center.
14 Aug 2026briBri's Production Prompt Engineering Method
Eliminate inconsistent AI agent behavior by applying a systematic set of prompt design rules and audit practices that produce repeatable, predictable outputs in production.
7 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 2026freeCodeCamp.orgSavvita LLM Fine-Tuning Pipeline Framework
Design and execute a complete, stage-correct LLM fine-tuning strategy — from selecting the right training stage to implementing parameter-efficient techniques — for any domain-specific AI use case.
25 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 2026AI AnytimeAI Anytime LLM Fine-Tuning End-to-End Framework
Given any target LLM, dataset, and compute budget, apply a structured methodology to fine-tune an open-source or closed-source large language model using LoRA, QLoRA, and low-code tooling — without memorising training data and with minimised GPU cost.
17 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 2026Tech With TimTech With Tim LLM Fine-Tuning to Ollama Pipeline
Fine-tune any open-source LLM on your own domain-specific data and deploy it locally via Ollama, without training from scratch.
11 July 2026AI EngineerSchmid Agent-Ready Engineering Framework
Diagnose and fix the five specific mindset and architecture gaps that cause experienced engineers to build unreliable AI agents, then redesign your agent system so it is production-ready.
30 May 2026Hetzel Agent Observability Differentiation Framework
Accurately diagnose whether a given AI agent system requires traditional observability tooling, agent-specific observability, or both — and design the right observability stack accordingly.
29 May 2026