Geeky Codes | AI Engineering, Data Engineering & Machine Learning Tutorials
Forget memorizing another list of LangChain APIs. If you understand these five concepts deeply, you can handle the questions that separate AI application builders from AI engineers. Introduction I’ve noticed a pattern in AI Engineer interviews. The candidate’s resume looks impressive: Then the interviewer asks: “Why does your RAG system still hallucinate even though retrieval … Read more
Learn how RAG works, why LLMs hallucinate, and build your first Retrieval-Augmented Generation pipeline in Python. Find all tutorials here Introduction Large Language Models (LLMs) have transformed how we build AI applications. Today, we can ask models to: Tools like ChatGPT, Claude, Gemini, and Llama make these tasks feel almost magical. But there’s one major … Read more
After interviewing 50+ AI Engineer candidates, I noticed a pattern: impressive GenAI projects can get you through the first 10 minutes—but strong fundamentals are what separate candidates who build AI systems from those who truly understand them. I’ve interviewed 50+ AI Engineer candidates over the past few months. Almost every resume looked impressive. RAG. AI … Read more
When your LLM returns malformed JSON, the problem isn’t always the model. Here’s how to build structured, validated, and production-ready outputs with Pydantic and Instructor. Your LLM application works perfectly in development. You deploy it. A few hours later, production logs start filling up with errors: Or perhaps: The frustrating part? The model’s answer looks … Read more