Topics

Choosing, evaluating, and measuring LLMs in production: decision frameworks, eval suites, metrics, and prompt engineering.

• 18 August 2026

  Choosing your LLM in 2026: GPT, Claude, Gemini, open-source — how I decide

• 22 July 2026

  Evaluating LLMs in production: building an eval suite that actually matters

• 28 May 2026

  Prompt engineering in 2026: what actually matters

• 20 May 2026

  Why LLM evaluation is the real engineering work

• 15 May 2026

  How to evaluate an AI model in production: metrics, evals, and pitfalls to avoid

Building and deploying AI agents in production: multi-agent systems, LangGraph, CrewAI, and field lessons.

• 27 June 2026

  What I wish I'd known before deploying my first production agent

• 24 June 2026

  AI agents in 2026: what changed (and what hasn't)

• 26 May 2026

  Building a multi-agent system: what I actually learned

• 22 May 2026

  LangGraph vs CrewAI: What I Learned in Production

• 22 May 2026

  Why a standalone AI agent isn't enough: the role of business context

• 18 May 2026

  MCP, LangGraph, agents: what real production projects actually teach you

Retrieval-Augmented Generation and intelligent memory systems: common mistakes, architectures, and strategies beyond basic RAG.

• 22 August 2026

  RAG in production: the 7 mistakes that break quality (and how to fix them)

• 30 June 2026

  RAG in production: the 5 mistakes I keep seeing everywhere

• 29 June 2026

  Why RAG Is No Longer Enough: Toward Intelligent Memory Systems

Claude Code, Cursor, MCP, and vibe coding: AI tools for engineers and what they actually change in the development workflow.

• 29 July 2026

  Coding with an AI agent: my daily Claude Code + MCP setup

• 27 July 2026

  Claude Code in production: my AI engineer workflow

• 21 June 2026

  Vibe coding: what it actually changes for product teams

• 25 May 2026

  Claude Code in production: lessons from the field

Launching, scoping, and driving AI adoption in the enterprise: POCs, ROI, training, team integration, and classic mistakes to avoid.

• 27 August 2026

  What a GenAI project costs in 2026: budget, timeline, ROI

• 22 June 2026

  Why I stopped doing POCs — and what I do instead

• 29 May 2026

  Measuring the ROI of an AI project: what leadership actually needs

• 24 May 2026

  When to automate and when not to: the real trade-off

• 23 May 2026

  Corporate AI training: what actually works

• 12 May 2026

  Generative AI in Business: Where to Actually Start

• 11 May 2026

  Why your AI proof of concept never makes it to production (and how to fix it)

• 10 May 2026

  How to integrate generative AI into a tech team in 2025

• 3 May 2026

  The 3 most common mistakes in enterprise AI projects

Deploying LLMs at scale: production architecture, lessons from Google and Microsoft, automation, and data quality.

• 27 June 2026

  What Google and Microsoft taught me about deploying at scale

• 19 June 2026

  What I learned deploying LLMs for real clients

• 18 June 2026

  AI and data: why data quality is the real bottleneck

• 27 May 2026

  From AI audit to deployment: anatomy of a real project