Development Intermediate
LLM
Large Language Model - Large-scale language models that can understand and generate human-like text, such as GPT-4, Claude, and Gemini.
Pronunciation
/ɛl ɛl ɛm/
"ell-ell-emm"
Listen on:
Forvo
What it is
LLM (Large Language Model) is a type of artificial intelligence model trained on massive amounts of text to:
- Understand natural language - Comprehend questions, instructions, and context
- Generate coherent text - Responses, code, documentation
- Reason - Follow logic, solve problems step by step
- Translate and summarize - Between languages and formats
LLMs are the “brain” behind ChatGPT, Claude, Gemini, and Copilot.
Pronunciation
IPA: /ɛl ɛl ɛm/
Sounds like: “ell-ell-emm” - pronouncing each letter separately
Also called:
- “Large Language Model” in full
- “Foundation model” (broader term)
How it works
┌─────────────────────────────────────────────────────────────┐
│ LLM ARCHITECTURE │
├─────────────────────────────────────────────────────────────┤
│ │
│ TRAINING (once) │
│ ┌──────────────────────────────────────────────────────┐ │
│ │ Internet, books, code, conversations │ │
│ │ → Trillions of text tokens │ │
│ │ → Weeks/months on thousands of GPUs │ │
│ └──────────────────────────────────────────────────────┘ │
│ │ │
│ ▼ │
│ TRAINED MODEL │
│ ┌──────────────────────────────────────────────────────┐ │
│ │ Parameters: 7B → 70B → 175B → 1T+ │ │
│ │ Knowledge "frozen" in weights │ │
│ └──────────────────────────────────────────────────────┘ │
│ │ │
│ ▼ │
│ INFERENCE (each use) │
│ ┌──────────────────────────────────────────────────────┐ │
│ │ User: "Explain what Kubernetes is" │ │
│ │ LLM: Predicts next token → next → ... │ │
│ │ Response: "Kubernetes is a platform..." │ │
│ └──────────────────────────────────────────────────────┘ │
│ │
└─────────────────────────────────────────────────────────────┘Major LLMs (2026)
| Model | Company | Parameters | Strength |
|---|---|---|---|
| GPT-5 | OpenAI | ~1T+ | General reasoning |
| Claude 3.5/4 | Anthropic | ~200B | Code, safety |
| Gemini 2 | ~1T+ | Multimodal, search | |
| Llama 3 | Meta | 8B-405B | Open source |
| Mistral | Mistral AI | 7B-22B | Efficiency, EU |
LLM vs SLM
| Aspect | LLM (Large) | SLM (Small) |
|---|---|---|
| Parameters | 70B - 1T+ | 1B - 13B |
| Hardware | Powerful GPUs, cloud | Laptop, phone |
| Cost | High ($0.01-0.10/1K tokens) | Low or free locally |
| Capability | Complex tasks | Specific tasks |
| Examples | GPT-4, Claude | Phi-3, Llama 7B |
Practical Example: LLM in Development
Before (without LLM)
Developer: Search Stack Overflow
Read documentation
Copy/adapt code
Debug manually
Time: 2-4 hours for simple featureNow (with LLM)
Developer: "Implement JWT authentication in Express"
LLM: Generates complete code + tests + documentation
Explains each part
Suggests security improvements
Time: 15-30 minutes with reviewLimitations
What LLMs can NOT do well
| Limitation | Description |
|---|---|
| Hallucinations | Make up information that sounds real |
| Limited knowledge | Training cutoff date |
| Complex math | Errors in long calculations |
| Private data | Cannot access your database |
| Real-time | Do not know current time |
Mitigations
┌────────────────────────────────────────┐
│ HOW TO IMPROVE LLMs │
├────────────────────────────────────────┤
│ │
│ + RAG (Retrieval) │
│ → Access to current documents │
│ │
│ + Tools/Function Calling │
│ → Execute code, APIs │
│ │
│ + Fine-tuning │
│ → Specialize in your domain │
│ │
│ + Agents │
│ → Plan and execute tasks │
│ │
└────────────────────────────────────────┘Related terms
- [[Agentic AI]] - LLMs that can act autonomously
- [[SLM]] - Small models for local devices
- [[MCP]] - Protocol to connect LLMs to tools
- [[Prompt Injection]] - Security attacks against LLMs
Remember: LLMs are powerful tools but not perfect. Always verify critical information and use your professional judgment.