Why is my Claude AI performance dropping?

Most perceived performance drops in Claude are not the model getting worse - they come from poor context engineering, bloated CLAUDE.md files, missing skills or plugins, and unstructured prompts. When the context window fills with noise, the model has less room to reason about your actual task. Cleaning up your project setup and using compaction strategies usually restores quality immediately.

What is the difference between prompt engineering and context engineering?

Prompt engineering is about writing a single instruction well. Context engineering is the broader discipline of shaping everything the model sees: the CLAUDE.md file, the skills loaded, the tool definitions, the conversation history, the files in the working directory, and how compaction trims old turns. Context engineering is what actually moves the needle on long-running agent workflows.

What are Claude Skills and Claude Plugins?

Skills are reusable capability bundles Claude can invoke when a task matches their description - they encode domain expertise (debugging, planning, brainstorming, etc.) and load on demand to keep context lean. Plugins extend Claude Code itself with new tools, slash commands, hooks, and MCP servers. Together they let you customize Claude without re-explaining the same instructions every conversation.

What is the CLAUDE.md file and why does it matter?

CLAUDE.md is a per-project (or global) instructions file that Claude Code automatically loads into every conversation in that directory. It is the most direct way to encode coding conventions, stack choices, do-and-don't rules, and project-specific context. A focused CLAUDE.md makes Claude faster and more accurate; a bloated one wastes context and degrades performance.

What is AI agent memory and how does it relate to context engineering?

AI agent memory is the mechanism by which an agent retains useful state across long sessions or multiple conversations - things like user preferences, project facts, and prior decisions. Context engineering decides how that memory is shaped: which turns are kept verbatim, which get summarized, and how the live context stays focused on what is actually load-bearing for the current task. Without good memory hygiene, performance degrades as the conversation grows.

Do I need Supabase or Dokploy to use Claude AI effectively?

No, but they pair well. Supabase gives you a managed Postgres backend with auth and storage that Claude can read and write through MCP, so your agents can persist data between runs. Dokploy lets you self-host the full stack on your own VPS, which is exactly what we cover in the Hermes Agent and OpenClaw tutorials. The combination is a clean foundation for production AI workflows.

Claude AI Beginner's Tutorial - Build a Working AI Agent Today - AI Academy Workshop

Welcome to the Claude AI Beginner's Tutorial, part of the AI Academy Workshop series. This tutorial will teach you how to build a working AI agent today, and it is designed for those new to AI tools and looking to learn how to use Claude AI, a powerful tool for context engineering and prompt engineering.

In this video, I will cover the basics of Claude Code, including how to create and manage your claude code md file, and how to integrate it with other tools like Supabase and Dokploy. You will also learn about Claude.md and how to use the Claude superpowers plugin to enhance your workflow. By the end of this claude code tutorial for beginners, you will have a solid understanding of how to use Claude AI and be able to apply your new skills to real-world projects.

Whether you are looking to learn Claude code context, Claude code skills, or simply how to get started deploying applications with AI for beginners, this video is for you. My goal is to provide a comprehensive Claude tutorial that covers everything you need to know to get started with Claude AI, including how to use the Claude Code skills, plugins, and how to deploy applications with Claude Code. So if you are ready to learn Claude and take your AI skills to the next level, be sure to watch this entire video and follow along with the tutorial.

Watch the Full Workshop

Full video walkthrough on YouTube: https://youtu.be/uo4rXq4JOJY

Why Performance Feels Like It's Dropping

Most of the time the model is not getting worse - the setup around it is. Common causes:

Bloated CLAUDE.md files filled with rules that no longer apply
Long conversations where compaction never kicks in cleanly
Missing skills and plugins that would have done the work for you
Unstructured prompts that bury the real task under context

Fix the context, fix the performance.

What You Will Learn in This Workshop

Context Engineering: beyond prompt engineering - shape every input the model sees, from CLAUDE.md to compaction.
Claude Skills and Plugins: how to install, build, and chain skills and plugins so Claude reaches for the right tool automatically.
CLAUDE.md Done Right: a clean, focused CLAUDE.md template - what to keep, what to cut, and what kills performance.
Supabase + Dokploy: a practical stack for persistent agent memory and self-hosted Claude Code workflows.

Topics Covered

How to use Claude AI effectively for everyday work and serious engineering tasks
Prompt engineering vs. context engineering - and why the second one matters more
Setting up a clean Claude Code project, including a Claude Code website tutorial
Using the Claude Superpowers plugin and other plugins to extend Claude Code
AI agent memory and how context engineering keeps long sessions performant
Connecting Supabase and Dokploy so your agents have a real backend

Final Thoughts

Claude AI has not gotten worse. The way most people use it has gotten messier. Tighten up your CLAUDE.md, install the skills and plugins that match your work, embrace context engineering over one-shot prompting, and keep memory under control. That is the real fix.

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