Introduction to Large Language Models (LLMs) & Prompt Engineering
Learn LLM architecture concepts, attention mechanisms, tokenizers, and advanced prompt engineering strategies.
LLM Architectures & Transformers
Modern Large Language Models (LLMs) are built on the Transformer architecture (introduced by Vaswani et al. in 2017). Unlike older RNNs that process text sequentially, Transformers use Self-Attention mechanisms to process entire sequences of text in parallel. This allows the model to capture long-range contextual relationships between words.
Key Concepts for Interviews: • Tokenization: Splitting text strings into integer IDs (tokens) using algorithms like Byte-Pair Encoding (BPE). • Temperature: Controls randomness. Low temperature (e.g. 0.2) makes responses deterministic and focused; high temperature (e.g. 0.8) introduces creativity and variance. • Top-P (Nucleus Sampling): Limits selection to a cumulative probability threshold.
Advanced Prompt Engineering Techniques
Prompt engineering is the practice of structuring queries to elicit optimal answers from LLMs:
- Zero-Shot: Prompting the model to solve a task without prior examples.
- Few-Shot: Providing 2-3 input-output examples in the context prior to the question.
- Chain-of-Thought (CoT): Instructing the model to "think step-by-step" before outputting the final answer. This forces the model to generate intermediate reasoning paths, drastically reducing errors in math or logic.
Chain-of-Thought Prompt Example
User Prompt: "Question: A juggler can juggle 16 balls. Half of the balls are golf balls, and half of the golf balls are blue. How many blue golf balls are there? Let's think step by step."
LLM Response: "1. The juggler has 16 balls total. 2. Half of the balls are golf balls: 16 / 2 = 8 golf balls. 3. Half of the golf balls are blue: 8 / 2 = 4 blue golf balls. Therefore, there are 4 blue golf balls."