🧠 What are Large Language Models?
Large Language Models (LLMs) are AI systems trained on massive amounts of text data that can understand, generate, and manipulate human language. They power ChatGPT, Claude, and most modern AI applications.
Think of LLMs as: Incredibly well-read assistants who have read billions of pages and can help with almost any text task, but sometimes confidently make things up.
✅ What LLMs Can Do
- • Generate human-like text
- • Answer questions
- • Write and debug code
- • Translate languages
- • Summarize documents
- • Reason through problems
❌ What LLMs Cannot Do
- • Access real-time information
- • Remember previous conversations
- • Guarantee factual accuracy
- • Perform actual computations
- • Learn from your data
- • Access external systems (without tools)
⚙️ How LLMs Work
1️⃣ Training Phase
LLMs learn patterns from trillions of words of text from the internet, books, and other sources.
2️⃣ Tokenization
Text is broken into tokens (chunks of characters) that the model can process.
3️⃣ Attention Mechanism
The model focuses on relevant parts of the input to generate contextually appropriate responses.
4️⃣ Generation
The model predicts the next most likely token, one at a time, to build complete responses.
🔍 Compare Popular LLMs
GPT-4
by OpenAI
✅ Strengths
- • Reasoning
- • Code generation
- • Analysis
⚠️ Weaknesses
- • Cost
- • Speed
- • Rate limits
Best for: Complex reasoning, code generation, detailed analysis
💰 Token Cost Calculator
📖 Key Concepts
Tokens
Basic units of text (≈0.75 words). LLMs process text as tokens, not words.
"ChatGPT is amazing!" = 5 tokens
Why it matters: Affects cost and context limits
Context Window
Maximum tokens an LLM can process in one conversation.
GPT-4: 128K tokens ≈ 96,000 words ≈ 200 pages
Why it matters: Limits conversation length and document size
Temperature
Controls randomness in responses (0 = deterministic, 2 = very random).
Low (0.2): Factual answers | High (1.5): Creative writing
Why it matters: Balance between consistency and creativity
Hallucination
When LLMs generate plausible-sounding but incorrect information.
Inventing fake citations or historical events
Why it matters: Critical risk in production systems
⚠️ Common Pitfalls
Trusting outputs blindly
LLMs can hallucinate facts. Always verify critical information.
Ignoring token costs
GPT-4 can cost $0.12 per page. Budget accordingly for production.
Expecting perfect consistency
Same prompt can give different outputs. Use temperature=0 for consistency.
Overloading context
Performance degrades near context limits. Keep conversations focused.
🎯 Key Takeaways
LLMs are pattern matchers: They predict likely text based on training, not true understanding
Choose models wisely: GPT-3.5 for chatbots, GPT-4 for reasoning, Claude for long docs
Tokens = Money: Optimize prompts and responses to control costs
Hallucinations are inevitable: Build validation and verification into your systems
Context windows matter: Plan for conversation length and document size limits