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
A practical AI/ML and GenAI interview preparation guide covering RAG, embeddings, Knowledge Graphs, LLM evaluation, system design, optimization, monitoring, and DSA AI/ML interviews are changing. Knowing definitions like “What is an embedding?” or “What is overfitting?” is no longer enough for senior AI/ML and GenAI roles. A recent Associate Architect (ML) interview experience at Quantiphi … Read more
Production agent latency is rarely just an LLM problem. Here’s how to find the real bottleneck. Your AI agent takes 12 seconds to answer a user. You check the LLM metrics. The model itself takes only 2 seconds. So where did the other 10 seconds go? This is one of the most important questions to … Read more
A practical guide to BM25, the ranking formula, TF-IDF, Python implementation, and why BM25 is still essential for modern RAG systems Introduction When people build a modern RAG system, the conversation often starts with embeddings: “Which embedding model should we use?” But before vector databases and semantic search became popular, search engines had already solved … Read more
The future of Retrieval-Augmented Generation (RAG) is shifting from reliance on dedicated vector databases to utilizing existing databases like PostgreSQL with pgvector. Production needs are leading to integrated systems that manage both vector similarity and traditional filtering. This consolidation promotes efficiency, reducing operational complexities and costs.
The content discusses the evolution of AI engineer interview questions, shifting from basic LLM concepts to intricate topics involving AI agents. Key areas include the differences between agents and chatbots, the importance of state management, decision-making in agent design, tool selection, and handling infinite loops. Understanding these aspects is crucial for aspiring AI engineers.
This content serves as a comprehensive guide to retrieval strategies in Retrieval-Augmented Generation (RAG) systems. It contrasts sparse retrieval, which emphasizes keyword matching, and dense retrieval, focused on semantic understanding. Both strategies are essential for answering user queries effectively. The tutorial also highlights hybrid approaches for optimal performance.
From documents to embeddings, retrieval, reranking, prompt construction, and grounded answers — a practical walkthrough of how modern RAG pipelines actually work. Introduction In the previous article, we looked at Why LLMs Hallucinate is and why RAG has become one of the most important architectures for building LLM applications. But knowing what RAG is isn’t … Read more
Learn why ChatGPT, Claude, Gemini, and Llama sometimes generate incorrect information, what causes hallucinations, and how production AI engineers minimize them using RAG, grounding, guardrails, and evaluation. Find all tutorials here Table of Contents Introduction If you’ve used ChatGPT long enough, you’ve probably experienced something like this. You ask: Who won the FIFA World Cup in … 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 the fundamentals, RAG, LLM inference, and GenAI architecture questions, here are 20 fresh AI Engineer interview questions designed to test whether you can reason about real-world AI systems — not just explain frameworks. Part 1: AI Engineer Interview Questions Part 2: AI Engineer Interview QuestionsPart 3: AI Engineer Interview QuestionsPart 4: AI Engineer Interview Questions Part 5: 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
A practical framework for debugging hallucinations in production RAG systems—from retrieval and chunking to prompts, generation, evaluation, and governance Part 1: AI Engineer Interview Questions Part 2: AI Engineer Interview QuestionsPart 3: AI Engineer Interview QuestionsPart 4: AI Engineer Interview Questions Part 5: AI Engineer Interview Questions One of the most interesting questions I have seen in … Read more
Large Language Models (LLMs) are pretrained on vast datasets to understand language and various concepts. Enterprises often fine-tune these models for specific domains. Techniques like full fine-tuning and Parameter-Efficient Fine-Tuning (PEFT) optimize model adaptation while minimizing costs and risks like catastrophic forgetting. Understanding these strategies is essential for AI applications.
“Our RAG chatbot worked perfectly in the POC.But once we scaled to 50,000 documents… accuracy dropped to 60%.” If you’ve worked with enterprise RAG systems, you’ve probably heard this story. And if you ask most engineers what went wrong, you’ll hear answers like: ❌ These sound smart❌ They sometimes help❌ But they miss the real … Read more