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Thoughts on machine learning, computer vision, embedded systems, and building for the web.
🚀 LangChain Series #3: Prompts Explained — Prompt Templates, Chat Prompts, Dynamic Prompting, Structured Outputs, and Output ParsersPrompts are the foundation of every LLM application.No matter how powerful a model is, the quality of its output depends heavily on the instruction…
Read on Medium🚀 LangChain Series #2: Models Explained — LLMs, Chat Models, and Embeddings with Practical ExamplesIntroductionWhen people start learning LangChain, they often think a “model” simply means ChatGPT or Gemini.In reality, LangChain provides a unified interface for working with diff…
Read on MediumWhy do we need Docker?Imagine you’re a chef who has created the perfect cake recipe. You want your friends to bake the same cake, but each friend has a different kitchen, equipment, and ingredients.As a result, the cake may not turn out exactly the way you intended.Software devel…
Read on MediumImagine you want to build an image classification model that can distinguish between cats and dogs, or perhaps mobile phones and tablets.The first thought that comes to mind is:“Why not train my own neural network?”Sounds simple, right?Not quite.The Problem with Training Your Own…
Read on MediumLarge Language Models (LLMs) such as GPT, Gemini, and Claude have made it easier than ever to build intelligent applications. However, developing production-ready AI systems often requires much more than simply calling an API.This is where LangChain comes inIn this article, we’ll…
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