Day 17 - Demystifying Large Language Models (LLMs): How They Learn and Think

Context:

Today, I explored how Large Language Models (LLMs) work behind the scenes and why they seem so intelligent when completing sentences or answering questions.

What I Learned:

• LLMs are trained on massive datasets — books, articles, websites — far beyond any single library.
• Inside the model are parameters (weights) that adjust whenever the model makes mistakes, improving predictions over time.
• Training happens on GPUs, compressing centuries of reading into weeks.
• Two key phases:
  • Pre-training: Learn to predict words and complete sentences.
  • RLHF (Reinforcement Learning with Human Feedback): Humans guide the model to be helpful, kind, and respectful.
• The magic lies in the Transformer architecture:
  • Attention Mechanism: Looks at all words in a sentence at once, understanding context (e.g., “river bank” vs “piggy bank”).
  • Feed-Forward Neural Network: Stores patterns learned during training.
• Backpropagation Algorithm adjusts weights when predictions are wrong, making the model smarter over time.

Why It Matters for QA / AI Testing:

• Understanding LLM internals helps testers design better validation strategies for AI-driven applications.
• Knowledge of RLHF and attention mechanisms ensures ethical and context-aware testing.
• Helps predict where models might fail — ambiguity, outdated knowledge, or bias.

My Takeaway:

LLMs aren’t magic — they’re a blend of massive data, smart algorithms, and human feedback working together to create intelligent responses.