RTK vs Native Claude Code Optimization: What to Fix Before Adding Another Hook
Before adding RTK to Claude Code, fix context, model choice, MCP overhead, and workflow packaging first. A practical guide for technical leaders.
Context Engineering
Context engineering is the discipline of getting the right information into a model at the right time. It is where chunking decisions determine accuracy, where token budgets become architecture constraints, and where most production LLM failures actually start.
Key themes
- Chunking strategies and retrieval architecture for document-heavy SME workflows
- Context window optimization and token economics as CTO-level budget decisions
- MCP as the integration layer that feeds team data into agentic tools securely
- RTK versus native tool optimization before adding complexity to dev stacks
- Standardizing the AI dev stack around context pipelines rather than model selection
Why it matters
A European engineering team running a frontier model with poorly chunked context will hallucinate just as confidently as one running a cheap local model. The articles here treat context engineering as a first-class architecture concern — covering the chunking, retrieval, and integration decisions that determine whether an LLM deployment actually answers the question or generates expensive fiction.
Articles (10)
What CTOs Should Standardize First in an AI Dev Stack
Most CTOs try to standardize the wrong thing first. They start with the vendor. Should we standardize on Copilot? Claude Code? Codex? Cursor?
MCP for Teams: The Integration Layer AI-Native Companies Need
In the last article, I wrote about Claude Desktop, the CLI, and OpenRouter as different layers in the same system. This article tackles the layer underneath all of them: the Model Context Protocol, and why **MCP for teams** is the integration layer AI-native companies need.
(Day 6/10) Context Windows & Retrieval: Feeding Models the Right Info
\*\*Definition:\*\* A context window represents the amount of text an AI model can process simultaneously—essentially its working memory, measured in tokens.
What Is Chunking in LLMs? Understanding the Foundation of AI Document Processing
You're building an AI system to analyze customer contracts. You upload a 200-page agreement to \[ChatGPT]\() and expect a comprehensive analysis. Instead, you get partial responses or errors. The problem? You've hit the chunk barrier—one of the most fundamental constraints in AI…
AI Tokens 2025: The Real Currency Every Leader Must Know
\## Tokens: The Real Currency of AI Work Let’s clear something up: when I talk about \*\*tokens\*\* at First AI Movers, I don’t mean crypto or blockchain. In AI, a token is a snippet of text — often part of a word — that language models process when generating responses. Why…
LLM Limits Solved: Complete Guide to AI Workarounds 2025
Master LLM limitations in minutes for enterprise success. Learn RAG, API integration, and memory solutions. Transform flawed tech into assets.
How LLMs Think: Complete Guide to AI Memory & Logic 2025
To effectively leverage Large Language Models, you must understand how they "think." Their intelligence is not human-like; it is a unique form of digital cognition with specific rules and limitations. Grasping these concepts is what separates the amateur user from the strategic…
Beyond Prompts: How Context Engineering Is Shaping the Next Wave of AI
Imagine if building an AI was less about crafting "magic" prompts and more like directing a blockbuster film, where the script, sets, and supporting actors all work together to make the hero shine. Welcome to the era of **context engineering**.
Why Context Windows Matter – Unlocking AI’s Long-Memory Power
Good morning! You’re reading _First AI Movers Pro_, the daily briefing that keeps AI pros ahead of the curve. Today’s main story demystifies the term “context window” and shows when knowing a model’s limit can save (or sink) your project.
Series in this topic
Prompt Engineering 10-Day Course
A hands-on prompt-engineering curriculum for health & fitness AI practitioners, covering fundamentals through production guardrails.
Quick reads
RTK vs Native Claude Code Optimization: What to Fix Before Adding Another Hook
Before adding RTK to Claude Code, fix context, model choice, MCP overhead, and workflow packaging first. A practical guide for technical leaders.