Blog & Resources
Practical guides on AI prompt engineering — debugging, architecture, security, testing, and incident response with Claude, ChatGPT, and Gemini.
Vibe Coding: What It Means and When It Actually Works
Describing what you want and accepting whatever the AI generates without reading it — genuinely fine for a weekend prototype, genuinely risky for anything that outlives the demo. Where the line actually is.
How to Reduce AI Token Costs When Coding with Claude or ChatGPT
Token costs scale with how much context you paste and how many turns a task takes — both are largely under your control. Practical cost reduction that doesn't sacrifice output quality.
RAG vs Fine-Tuning vs Prompting: Which Should You Use?
Three different ways to get an LLM to know something it doesn't already know, or behave in a way it doesn't already behave — and they solve different problems, not the same one at different price points.
How to Build an AI Agent: A Beginner's Guide
An 'agent' is a normal LLM call plus two things: tools it can invoke, and a loop that lets it use their results to decide what to do next. Demystified, with the actual failure modes to design around.
Prompt Injection: What It Is and How to Defend Against It
Any AI feature that reads untrusted input — a user message, a scraped webpage, an uploaded document — can have its instructions hijacked by text embedded in that input. What this attack actually looks like, and the concrete defenses that work.
AI Pair Programming: How to Actually Work with an AI Coding Assistant
Treating an AI assistant like a search engine or like an infallible senior engineer both fail the same way. The working model that actually holds up: a fast, literal junior who needs precise direction and never gets tired of review.
Best AI Tools for Software Developers in 2026
A roundup organized by job, not hype — which tool actually solves which problem, so you're not adopting five overlapping subscriptions that do the same thing.
10 ChatGPT & Claude Prompts for Debugging Code Faster
"Why doesn't this work" wastes a turn. These ten prompts are structured around what actually needs to be true for AI-assisted debugging to work — evidence first, guessing last.
What Is Claude AI? A Practical Guide for Developers
Claude is Anthropic's family of large language models — but what does that actually mean for how you'd use it day-to-day as a developer? A practical, no-hype explanation.
How to Write Better AI Prompts: A Beginner's Checklist
Before you send that prompt, run it through five questions. Most disappointing AI output traces back to one of these being skipped, not to the model being incapable.
Prompt Engineering Certification: Is It Worth It? (And How to Get One Free)
A certificate doesn't prove skill by itself — it proves you completed a specific structured curriculum. Whether that's worth having depends entirely on what's actually behind it.
How to Learn Prompt Engineering for Free in 2026
You don't need a paid course to start. A concrete, no-cost path — what to read, what to practice, and how to tell if a free resource is actually teaching the skill or just listing prompt examples.
Best ChatGPT & Claude Prompts for Developers (Copy-Paste Templates)
Generic 'write me some code' prompts produce generic results. These templates are structured around the specific failure mode each one prevents — copy, adapt the bracketed parts, and use.
Will AI Replace Software Developers? What Engineers Should Actually Do
The honest version of this answer isn't 'no, don't worry' or 'yes, panic' — it's that the job is changing shape, and the engineers doing fine are the ones who changed with it early.
GitHub Copilot vs Cursor vs Claude: Best AI Coding Assistant in 2026
These solve different problems, not the same one — autocomplete, whole-repo editing, and deep reasoning. Which to pick depends on what kind of work you're doing, not which is 'best' in the abstract.
Is Prompt Engineering a Good Career in 2026?
The standalone job title mostly disappeared. The skill it names became more valuable, not less. An honest look at where prompt engineering actually pays off — and where it doesn't.
How to Become a Prompt Engineer in 2026: A Step-by-Step Path
"Prompt engineer" isn't usually a standalone job title anymore — it's a skill layered onto an existing role. Here's the actual path: what to learn, in what order, and how to prove it.
Load Testing with AI: Generating Realistic Performance Test Scenarios
A load test that hammers one endpoint at a constant rate proves almost nothing about production behavior. How to design scenarios that actually resemble real traffic, and generate them with AI instead of hand-writing k6 scripts from scratch.
Golden Master Testing: Safely Refactoring Legacy Code Without Specs
You can't write example-based tests for behavior nobody can fully describe. Golden master (approval) testing captures what the code actually does today, so a refactor can be verified without ever writing down the spec.
Memory Leak Analysis: Debugging .NET Memory Issues with AI
A managed runtime doesn't stop memory leaks — it just changes what causes them. Event handlers, static caches, and captured closures are the usual suspects. How to diagnose them from a memory dump with AI as a reasoning partner.
Saga Pattern Explained: Managing Distributed Transactions with AI
Once a business process spans multiple services, there's no database transaction big enough to cover it. The saga pattern — and its hardest part, writing compensations — explained with a worked AI prompt.
The Transactional Outbox Pattern: Reliable Event Publishing with AI
Writing to your database and publishing an event are two separate operations — and the gap between them is where events get silently lost. The outbox pattern closes it. How to implement it correctly with AI.
Writing Architecture Decision Records (ADRs) with AI
The decision nobody wrote down is the decision that gets silently reversed by someone who didn't know why it was made. A practical guide to ADRs, and how AI removes the excuse not to write them.
Debugging Deadlocks and Race Conditions with AI
Concurrency bugs don't reproduce on demand and rarely show up in a single stack trace. A structured approach to feeding thread dumps and timing evidence to AI instead of guessing.
AI Code Review: Catching Bugs Before They Ship
An AI reviewer catches different mistakes than a human one — it doesn't get tired, doesn't skim on a Friday afternoon, and doesn't know your team's unwritten context. How to use it as a first pass, not a replacement.
Understanding Context Windows and Token Limits: A Developer's Guide
Why a model that 'read the whole file' can still miss something in it, what a token actually is, and the practical rules for managing context in long AI-assisted coding sessions.
API-First Development: OpenAPI Contracts with AI
Designing the contract before the implementation catches integration mismatches while they're free to fix. How to generate, review, and keep OpenAPI specs honest with AI in the loop.
Event Storming with AI: Facilitating Domain Discovery Sessions
Event storming surfaces the real business process before a line of code is written. How to use AI as a facilitation partner — generating domain events, spotting missing actors, and turning sticky notes into a bounded context map.
Chain-of-Thought Prompting: A Practical Guide for Developers
Asking an LLM to reason step by step before answering measurably improves accuracy on multi-step engineering tasks. When it actually helps, when it's wasted overhead, and how to prompt for it well.
Zero-Downtime Deployments: Feature Flags, Blue-Green, and AI-Generated Rollout Plans
Most production outages happen during deploys, not because of them in the abstract — because of unsynchronized schema changes, missing backward compatibility, or an all-or-nothing cutover. How to avoid all three, with AI doing the rollout planning.
Legacy Modernization with AI: The Strangler Fig Pattern
Big-bang rewrites fail far more often than they succeed. The strangler fig pattern — incrementally routing traffic away from legacy code until it can be retired — is safer, and AI removes most of its planning overhead.
Prompt Engineering Interview Questions and Answers (2026)
The prompt engineering and AI-assisted development questions engineers are actually being asked in interviews right now — with concrete, not textbook, answers.
Mutation Testing Explained: Why Code Coverage Lies to You
95% code coverage tells you almost nothing about whether your tests would catch a real bug. Mutation testing does. How it works, how to run it with Stryker.NET, and how AI closes the gap it finds.
Property-Based Testing with AI: A Practical FsCheck Guide
Example-based tests only check the cases you thought of. Property-based testing checks the invariants that must always hold — and AI is unusually good at generating them. A practical guide with FsCheck for .NET.
How to Prevent AI Hallucinations in Code Generation
Why LLMs invent function signatures, library methods, and config options that don't exist — and the five concrete prompting techniques that shut it down in real codebases.
Writing a SEV1 Incident Post-Mortem with AI: Prompt Templates for On-Call Engineers
How on-call engineers can use Claude for SEV1 rapid triage and blameless post-incident root-cause reports — prompt templates that speed up incident response without skipping rigor, with a full worked timeline.
Prompt Engineering for QA: Property-Based, Mutation, and Contract Testing with AI
How QA and test engineers can use Claude to generate property-based tests, mutation tests, and consumer-driven contract tests — beyond basic example-based unit tests, with worked prompt-and-output examples.
.NET Framework to .NET 8: Legacy Modernization with AI (Strangler Fig)
Using AI to plan and execute a .NET Framework to .NET 8 migration with the Strangler Fig pattern — readiness assessment, incremental CQRS extraction, and zero-downtime cutover prompts, with a worked sequencing example.
STRIDE Threat Modeling with AI: A Step-by-Step Guide
How to run a STRIDE threat model with Claude — Spoofing, Tampering, Repudiation, Information disclosure, Denial of service, Elevation of privilege — with a full worked example against a login endpoint.
CQRS and Domain-Driven Design with AI: A Prompt Engineering Approach
How to use Claude for Domain-Driven Design and CQRS: event storming facilitation, rich domain entities, and full CQRS vertical slices — with prompt patterns and a worked example that avoids anemic models.
AI-Assisted Debugging: A Scientific-Method Prompt Framework
How to prompt Claude or ChatGPT for debugging production issues without inviting hallucinated root causes — a scientific-method framework covering stack traces, memory issues, and deadlocks, with a full worked example.
Claude vs. ChatGPT vs. Gemini for Software Development: Which to Use When
A practical, vendor-neutral comparison of Claude, ChatGPT, and Gemini for coding, architecture, debugging, and testing — and why the underlying prompt engineering technique matters more than the model you pick.
What Is Prompt Engineering? A Practical Guide for Software Engineers
Prompt engineering explained for developers: what it actually is, why it's a real engineering discipline, and how to structure prompts that produce reliable, production-grade output from Claude, ChatGPT, or Gemini.