Production AI & LLMs
Getting an LLM feature through its first week of real traffic is a different job from building the prototype. Here I cover RAG failures, vector databases, cost optimization, evaluation, and the architecture decisions that hold up under load.
The security requirements for deploying autonomous AI agents in production: identity, least privilege, prompt injection defenses, human approval, audit logs, and more, mapped to OWASP and NIST guidance.
A hiring scorecard for choosing an AI engineer to build production agents with human fallbacks: what to weigh, questions to ask, and red flags that predict a fragile demo.
Learn how AI document processing automates data extraction, document classification, verification, and workflows while reducing manual errors and processing time.
Your RAG demo worked. Production answers are wrong or empty. Separate retrieval, ranking, chunking, freshness, and generation. Know when to rescue.
Most AI courses teach clever prompts. Real jobs run on messy GST invoices, vendor contracts, and WhatsApp threads with actual consequences. Here's why I built Praxismith: two courses on turning fragile AI chats into systems you can defend to your manager.
How to build an AI MVP that actually works — the 9-step process, real cost ranges, tech stack decisions, and the build vs. buy choices that determine whether your AI product ships in 8 weeks or 8 months.
AI chatbot development services explained: how they work, rule-based vs AI, cost, ecommerce use cases, and how to choose the right development partner.
Most developers build agents with tools. That's not enough. Here's the architectural difference between agent tools and agent skills — and why getting this wrong burns tokens, degrades accuracy, and makes your agents brittle in production.
Claude doesn't count messages like ChatGPT — it counts tokens, and one conversation can burn 10x more than another. Here are 5 tricks that cut your token usage by 80% or more.
The economics that made your demo cheap fall apart at scale. Context window bloat, wrong model for the task, and zero caching are costing you 60–90% more than necessary. Here are five levers — with Node.js code — to cut your LLM bill without touching your output quality.
Webflow handles your frontend beautifully. n8n handles the logic. Claude handles the intelligence. Here's how to wire them together into automation pipelines that actually work.
Your regular database can't handle meaning — only words. Here's what vector databases actually do, how HNSW works, and when to use one in your stack.
A practical developer-first guide to Software 3.0: LLMs, context windows, retrieval, latency, evals, agent loops, and tool calling.
AI spend without guardrails turns innovation into margin risk. Learn the architecture shift—filters, state machines, and private models—that restores cost control.
85% of GenAI launches implode. Deploy bullet-proof JavaScript patterns that end downtimes, kill hidden fees, and save your next release.
Learn how to design, implement, and scale GPT-powered automation workflows for business efficiency using LangChain, Zapier, and n8n.
Feeling frustrated by your AI coding assistant? Learn the 'Architect and the Intern' mental model, a powerful workflow to fix AI hallucinations, boost developer productivity, and master tools like GitHub Copilot and ChatGPT. Stop 'vibe coding' and become the architect of your code.
Discover the best AI tools for developers in 2025, including ChatGPT Plus, Cursor.ai, GitHub Copilot, and more. Learn how these tools enhance coding efficiency through real-world insights and performance reviews.
Learn how to create a spec-compliant MCP server from scratch using TypeScript, Express, and Zod, and use it seamlessly with Cursor.
Explore the key differences between Agent-to-Agent (A2A) communication and Modular Contract Protocols (MCP) — and why you need both to build powerful AI systems.
Learn how I wrapped the Hacker News API into a modular, LLM-ready MCP server with Zod contracts and OpenAPI support using zod-openapi.