What is an LLM, and Selection

​What exactly is an LLM, and how do you choose the right one for your project?

​In this practical and architecture-focused session, we break down Large Language Models from first principles.

​We’ll explore:

1. ​What an LLM really is
   Transformer architecture, tokenization, embeddings, and probabilistic generation — explained in a clear, applied way.

2. ​Model Categories

   • ​Closed-source APIs (e.g., GPT-style models)

   • ​Open-source models (e.g., LLaMA variants)

   • ​Specialized / fine-tuned domain models

3. ​How to Select the Right Model

   • ​Reasoning capability vs speed

   • ​Cost per token

   • ​Latency requirements

   • ​Data privacy & deployment control

   • ​Context window size

   • ​Fine-tuning vs RAG compatibility

4. ​Real-World Scenarios

   • ​Chatbots

   • ​AI copilots

   • ​Enterprise knowledge assistants

   • ​Agentic workflows

​🎯 Who This Is For:

• ​AI builders

• ​Developers

• ​Product managers

• ​Tech founders

• ​Anyone deploying AI into production

​This is not a hype session.
It is a decision-making session.

​If you're building AI applications, selecting the wrong LLM can cost you time, money, and performance.

​Let’s make that decision correctly.