A comprehensive guide to becoming a Full Stack AI Engineer in 2026. Learn the complete stack from frontend to infrastructure, with practical code examples using GPT-5, Python, FastAPI, LangChain, and Next.js for building AI-powered applications.
Category: Tutorial
The Complete Evolution of OpenAI’s GPT Models: From GPT-1 to GPT-5.2
A comprehensive journey through OpenAI’s GPT model evolution from 2018 to 2025. From the 117M parameter GPT-1 to today’s trillion-parameter GPT-5.2, explore the revolutionary advances in context windows, pricing, capabilities, and market adoption that have transformed AI.
Observability Practices in AI Engineering: A Complete Guide to LLM Monitoring
Master AI observability with this comprehensive guide. Compare Langfuse, Helicone, LangSmith, and other tools. Learn which metrics matter, how to build evaluation pipelines, and implement production-grade monitoring for LLM applications.
Alternative Cloud AI Platforms: IBM watsonx, Oracle OCI, Databricks & Snowflake Deep Dive
Beyond AWS, Azure, and GCP—explore IBM watsonx, Oracle OCI, Databricks, and Snowflake AI platforms. Complete guide with architectures, code examples, and when to choose each platform.
DIY LLMOps: Building Your Own AI Platform with Kubernetes and Open Source
Build a production-grade LLMOps platform using open source tools. Complete guide with Kubernetes deployments, GitHub Actions CI/CD, vLLM model serving, and Langfuse observability.
Cloud LLMOps: Mastering AWS Bedrock, Azure OpenAI, and Google Vertex AI
Deep dive into cloud LLMOps platforms. Compare AWS Bedrock, Azure OpenAI Service, and Google Vertex AI with practical implementations, RAG patterns, and enterprise considerations.
MLOps vs LLMOps: A Complete Guide to Operationalizing AI at Enterprise Scale
Understand the critical differences between MLOps and LLMOps. Learn prompt management, evaluation pipelines, cost tracking, and CI/CD patterns for LLM applications in production.
Building AI Agents: A Complete Code Review Assistant from Scratch
Hands-on tutorial building a production-ready AI agent. Create a code review assistant with tool use, error handling, caching, and GitHub integration.
Agentic AI Explained: Building Autonomous Systems That Plan, Act, and Learn
Move beyond simple chat to autonomous AI agents. Understand ReAct, multi-agent architectures, memory systems, and what actually works in production today.
GenAI Frameworks Compared: Building Real Applications with LangChain and LlamaIndex
Compare LangChain, LlamaIndex, Semantic Kernel, and more. Learn when to use each framework and build production-ready RAG systems with practical code examples.