AI Automation

Learn how to evaluate AI workflows in production using task-based metrics, human review, regression checks, and business-aligned quality thresholds.

A practical guide to designing and shipping AI workflows inside a production SaaS app, with orchestration, fallback logic, evaluation, and user trust considerations.

A production guide to shipping AI features safely with guardrails, confidence thresholds, fallback paths, auditability, and human-in-the-loop review.

A practical guide to context engineering for enterprise AI applications, covering retrieval, memory, permissions, task framing, and context window tradeoffs.

Learn the core security patterns for production MCP tools and servers, including auth, permissions, input validation, tool isolation, audit logs, and prompt-injection defenses.

Learn how MCP servers can connect Next.js tooling, project context, and AI workflows so agents can inspect apps, use tools, and support real developer tasks.

Learn how to design tool permissions, execution boundaries, and audit logging for AI agents so autonomous workflows stay controlled and reviewable.

Build an AI content automation system with multi-step generation, human-in-the-loop review, and multi-channel publishing. Automate content workflows end to end.

Build an AI document processing pipeline with OCR, classification, and data extraction. Automate invoice, receipt, and contract processing with LLMs and vision.

Ship AI systems to production with confidence. Learn security hardening, cost optimization, rate limiting, and horizontal scaling strategies for LLM apps.

Build powerful AI automation workflows by combining n8n's visual automation with LangChain's AI capabilities. Create no-code and code-first hybrid pipelines.

Build an AI-powered code review agent that automatically reviews GitHub pull requests. Detect bugs, suggest improvements, and enforce coding standards at scale.

Master LLMOps practices for evaluating, tracing, and monitoring AI systems in production. Set up Langfuse observability and automated evaluation pipelines.

Explore multi-agent orchestration patterns with LangGraph and CrewAI. Learn supervisor, sequential, and parallel architectures for complex AI workflows.

Build a multi-tool AI agent using MCP and Claude. Learn to create custom MCP servers, connect multiple tools, and orchestrate complex agentic workflows.

Build MCP servers with TypeScript to extend AI assistants. Learn Anthropic's Model Context Protocol for standardized tool integration and resource access.

Learn to build AI agents with tool calling using Vercel AI SDK. Implement function calling, structured outputs, and agentic loops for autonomous tasks.

Build a production-ready RAG chatbot using Next.js, pgvector, and OpenAI. Complete tutorial covering ingestion, retrieval, streaming responses, and deployment.

Master vector databases and embeddings for AI applications. Compare pgvector, Pinecone, and Weaviate with practical implementation examples and benchmarks.

Step-by-step guide to building RAG pipelines with vector databases and embeddings. Learn chunking strategies, retrieval methods, and generation patterns.

Discover the shift from prompt engineering to context engineering. Learn how to structure context windows for reliable and consistent AI outputs.

Learn the fundamentals of AI automation and large language models. Understand how LLMs from OpenAI and Anthropic work and power modern AI applications.