Ahmad Wael explores why GraphRAG for Developers is the key to building cost-efficient, high-recall retrieval systems. Moving beyond vanilla RAG, this post covers the importance of data contracts, schema validation in PHP, and production lessons from the December TDS newsletter to ensure your WordPress AI integrations remain stable and scalable.
Stop overengineering your AI. Many developers throw complex RAG pipeline features like query optimization and neighbor expansion at every problem, but the data shows it often isn’t worth the 40% cost increase. Learn when these add-ons actually improve faithfulness and when they just add latency to your WordPress integrations.
Stop fitting straight lines to curved data. This guide explains why Scikit-Learn’s SplineTransformer is superior to polynomial regression for handling non-linear trends. Learn to avoid Runge’s Phenomenon, implement B-spline pipelines, and use periodic extrapolation for cyclical data like seasonal sales or daily demand. Professional advice for building stable, production-ready machine learning models.
Stop shipping AI agents blindly. Ahmad Wael explains how to use the NeMo Agent Toolkit (NAT) to implement deep observability and trajectory evaluation. Learn to use Arize Phoenix and W&B Weave to identify bottlenecks, compare model performance, and ensure your LLM applications are production-ready through data-driven metrics.
Stop wasting time re-typing repetitive AI prompts. Learn how to leverage Slash Commands in tools like Cursor and Claude Code to automate WordPress development, from scaffolding unit tests to analyzing debug logs. Senior dev Ahmad Wael shares his “Golden Prompts” for building a faster, more consistent technical workflow.
Stop guessing with your prompts. Automatic Prompt Optimization uses systematic algorithms like HRPO to refine multimodal vision agents, replacing manual trial-and-error. Learn how to implement the Opik-optimizer SDK with GPT-5.2 to improve accuracy by over 150% in production autonomous driving scenarios. A technical guide from a senior developer’s perspective.
The Penpot MCP server experiment marks a major shift in AI-powered design workflows. By leveraging the Model Context Protocol, Penpot provides a secure, structured bridge for LLMs like Claude to interact with real design data. This senior dev’s take explores why design-expressed-as-code is the only way to eliminate AI hallucinations and technical debt.
HNSW Recall Degradation is a silent killer in scaling RAG systems. As vector databases grow, retrieval quality drops even when latency remains stable, leading to LLM hallucinations. Learn why you must tune ef_search and use hybrid retrieval architectures to maintain context quality in production WordPress AI applications.
Stop guessing and start solving. Explore how Advent of Code 2025 problems like Tachyon Manifolds and Factory Optimization reveal the importance of Algorithmic Thinking in Data Science. Learn to use MILP, Union-Find, and Dynamic Programming to build enterprise-grade logic that scales without crashing your execution stack.
Ahmad Wael shares a pragmatic guide to Drift Detection in machine learning systems. Learn the difference between data drift and concept drift, and discover how to use statistical tests like K-S and PSI to identify model degradation before it impacts your business revenue and system stability.