llm

5 AI Engineer Interview Concepts You Must Know in 2026

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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

How to Fix LangChain OutputParserException in Production LLM Pipelines

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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

RAG with Llama 2, LangChain and ChromaDB: A Practical Implementation

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Introduction Learn how Retrieval-Augmented Generation (RAG) works by building an end-to-end RAG pipeline with Llama 2, LangChain, Hugging Face embeddings, and ChromaDB — and understand how this early architecture compares with modern production RAG systems. Originally published: August 2, 2024Updated: September 2026 Introduction Large Language Models (LLMs) can answer questions, summarize text, generate code, and … Read more