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The Claude Code Project Template That Closes the Loop: PRD → Plan → Build → Validate → Track

Most people use Claude Code like a chat window: type a request, accept the diff, repeat until it works or breaks. That's fine for a one-off script. For a real project it falls apart fast — the agent forgets decisions, rebuilds things it already built, and you lose track of what's actually done.

The fix isn't a smarter model. It's a project structure that gives the agent memory, process, and guardrails. I found a template that nails this: code-template — a Claude Code starter that defines the full coding loop with 5 files and 3 slash commands. This post walks through how it works and why each piece earns its place.

Hybrid RAG in Production: A Full n8n + Airflow Implementation

You've got the concepts — chunking, hybrid search, reranking, GraphRAG. This is the wiring diagram. A complete hybrid RAG system you can import and run: ingestion on Apache Airflow, the query pipeline on n8n, retrieval fused across dense, BM25, GraphRAG, and live web.

This is the companion build to Agentic Retrieval: The Complete Guide. Every technique there, ported into nodes and DAG tasks you can click, run, and adapt — with a local-first swap for every cloud component.

Building a Production RAG App: The Decisions, the Traps, and the Fixes

I built a full-stack RAG application — streaming chat, document ingestion, hybrid search, sub-agents, admin-managed model config — and most of the hard parts had nothing to do with calling an LLM. The LLM call is 20 lines. The other 95% is retrieval quality, ingestion correctness, and not blocking your event loop.

This walks through the real decisions and the traps I hit: where naive RAG breaks, and the exact fix that shipped. Every number here is from the running code, not a whitepaper.

All the code is open source: github.com/pkhamdee/rag. Every snippet below links to the file it came from, so you can read the full implementation in context.

Credit: the template's structure and workflow originate from the Claude Code Agentic RAG Masterclass

Agentic Retrieval: The Complete Guide from Document Ingestion to Compiled Knowledge

Naive RAG fails roughly 40% of the time at retrieval. Not because the LLM is bad — because what you hand it is bad. Wrong chunks, missing context, no awareness of what it doesn't know. Agentic retrieval fixes the retrieval layer, not the generation layer.

This guide covers the entire pipeline: from ingesting a raw PDF to deploying an agent that queries vector stores, SQL databases, knowledge graphs, and pre-computed summaries — and knows which one to use for each question.

The Math Behind an Entire LLM: From Tokens to Fine-Tuning

Most LLM explainers either skip the math entirely or dump you into a research paper. This one is neither. We'll walk every equation with real numbers, using the same three-token sentence — "The cat sat" — all the way from raw text to gradient updates.

By the end, you'll be able to trace any weight in any layer and know exactly what changed it, why, and by how much.

Build a Second Brain with Claude Code and Obsidian

Most note-taking apps are graveyards. You clip an article, it sits in a folder, you never read it again. The knowledge doesn't connect to anything. It doesn't compound. After three years you have 4,000 notes and can't find anything.

This guide builds something different: a self-maintaining knowledge base where Claude Code acts as your librarian. You dump raw material in. Claude reads it, writes structured wiki articles, cross-links related concepts, and files everything. You ask questions. The answers become new notes. The library grows smarter every time you use it.

The architecture is based on Andrej Karpathy's LLM Wiki pattern: a persistent, compounding knowledge base where an LLM does the bookkeeping you'd never do yourself.

Agent-Driven Software Factory: Replace Vibe Coding With a 12-Agent Pipeline

Vibe coding is not a software development process.

Fire a prompt, accept the result, ship it. Fast in the same way skipping tests is fast — until something breaks and you can't explain why. The output is plausible-looking code with no validated problem, no verified requirements, no threat model, no acceptance tests, no deployment strategy. One feature. Eleven things that were supposed to happen but didn't.

The Software Factory Agent replaces that with a 12-agent pipeline. One developer runs the chain. Specialized agents handle market research, product scoping, UX design, security, implementation, testing, validation, and deployment — each in its own clean context window with only the tools it needs.

Two modes. Discovery mode (12 agents) validates the market, defines the product, and designs the UX before any engineering starts. Feature mode (9 agents) skips straight to implementation for incremental work on an existing product.

The Karpathy Skill: Four Rules That Make You a Better AI-Era Engineer

Most developers use AI coding assistants wrong.

They fire off a vague prompt, accept the first 200-line response, and then spend the next hour debugging why the "helpful" code broke three other things. The tool is fast — the outcome is slow.

Andrej Karpathy — the Stanford AI researcher who co-founded OpenAI, led Tesla's Autopilot team, and built NanoGPT from scratch — distilled decades of engineering intuition into four rules that are now circulating as a CLAUDE.md template for AI coding assistants. These rules are not about AI. They are about how senior engineers think — and they happen to be exactly what you need to get great results from any AI coding tool in 2026.

This post unpacks every rule with the why, concrete examples, and practical habits you can start using today.

Prompt Engineering: The Anthropic Playbook

There is no magic phrase that makes an LLM smarter. There is no secret syntax, no special keyword, no hidden trick that unlocks better results.

What there is: a systematic discipline for giving models what they actually need to do good work. That discipline has a name — prompt engineering — and Anthropic's Harrison Chase and Christiaan Ryan spent an entire workshop teaching it the right way, using a real production example, and showing exactly where simple intuitions fail.

Source: Harrison Chase & Christiaan Ryan, Anthropic — "Prompting 101" (YouTube)

Building Effective AI Agents: The Anthropic Playbook

Most teams building AI agents are solving the wrong problem.

They spend months wiring together orchestration frameworks, reflection loops, and multi-agent graphs — before they've verified that the simplest version of their agent actually works. Then they wonder why the system is expensive, slow, and impossible to debug.

Barry Zhang from Anthropic gave a talk that cuts through all of that. The core message was blunt: most teams are building agents too early, and when they do build them, they build them wrong.

Source: Barry Zhang, Anthropic — "How We Build Effective Agents" (YouTube)