Week 33 : AI Knowledge Sharing Post

Understanding LLMs & Building Your Own

🚀 What is an LLM?
A Large Language Model (LLM) is an AI system trained on massive text datasets to understand and generate human-like language. Examples include GPT-4, Claude, Gemini, and LLaMA. They power chatbots, summarization tools, coding assistants, and more.

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How does an LLM work?

1. Training Phase – The model learns language patterns from billions of sentences.
2. Fine-Tuning – Adjusting for specific tasks or industries.
3. Prompting – Providing inputs (questions/commands) to get useful outputs.

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Using ChatGPT to Create Your Own LLM

You don’t always need to train a model from scratch (which can cost millions 💸).
Instead, you can:

• Leverage ChatGPT + APIs to build prototypes.
• Fine-tune existing open-source models like LLaMA 2, Mistral, or Falcon on your domain-specific data.
• Use LangChain or LlamaIndex to connect your LLM with private documents.
• Host your model using Hugging Face Spaces or OpenAI fine-tuning endpoints.

💡 Example:

• Gather your industry-specific FAQs
• Use ChatGPT to reformat and clean data
• Fine-tune an open-source model with that data
• Deploy it via API for internal or customer use

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Where to Start Learning LLMs (Free Video Resources)

🎥 Beginner Level

• Intro to Large Language Models – Google Cloud Tech
• What are LLMs? – Hugging Face

🎥 Intermediate Level

• Fine-tuning LLaMA 2 – Hugging Face
• LangChain for Beginners

🎥 Advanced Level

• Build Your Own LLM – Andrej Karpathy
• Training LLMs from Scratch – Yannic Kilcher

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🔑 Takeaway:
You don’t have to be OpenAI to build something powerful — combining existing LLMs + your data can give you a custom AI assistant in weeks, not years.

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#AI #LLM #MachineLearning #ChatGPT #GenerativeAI #LangChain #FineTuning #AIForBusiness #HuggingFace #BoopeshVikram #www.boopeshvikram.com