Halfway: where are we?
We’re three weeks in and you already have solid foundations. Let’s recap:
Week 1 you understood what AI is, where it comes from, and that you already use it every day. Week 2 you discovered how LLMs work: tokens, context windows, models, prompts. Week 3 you learned how to access them: providers, APIs, free options, local models.
Today we consolidate everything with a global view.
The complete diagram
YOU (human or agent)
│
▼
INTERFACE (web, Telegram, app, terminal)
│
▼
FRAMEWORK (OpenClaw, LangChain, or the API directly)
│
▼
PROVIDER (OpenAI, Anthropic, Groq, NVIDIA NIM, local Ollama)
│
▼
MODEL (GPT, Claude, LLaMA, Mistral, Qwen...)
│
▼
RESPONSE (text generated token by token)
Each layer is independent of the others. You can switch providers without changing frameworks. This modularity is what makes the ecosystem so flexible.
Provider comparison table
| Provider | Models | Price | Speed | Free tier | Best for |
|---|---|---|---|---|---|
| OpenAI | GPT-4, GPT-5 | High | Good | Limited (web) | Maximum quality |
| Anthropic | Claude | High | Good | Limited (web) | Complex instructions, long docs |
| Google AI | Gemini | Mid-low | Good | Yes (API) | Starting free |
| Groq | LLaMA, Mistral | Low | Very fast | Yes (API) | Speed |
| NVIDIA NIM | Kimi, LLaMA | Low | Acceptable | Yes (API) | Free agent |
| OpenRouter | All | Variable | Variable | No | Easy model switching |
| Ollama (local) | All open | Zero | Hardware-dependent | N/A | Privacy, zero cost |
Decision tree
Do you have a budget? If no → local Ollama (if you have 16GB+ RAM) or Google AI Studio / NVIDIA NIM.
What do you need? Maximum quality → Claude or GPT. Speed → Groq. Privacy → Ollama. Cheap volume → inference provider with open model.
How complex is your use? Simple → 7-8B model. Intermediate → 30-70B or mid-range commercial. Advanced → top-tier models.
Suggested practical exercise
Try three free models and compare responses:
- ChatGPT (chat.openai.com)
- Claude (claude.ai)
- Google AI Studio (aistudio.google.com)
Ask the same question to all three. Compare the responses: which is clearest? Most accurate? Which tone do you prefer?
Week 3 glossary
- Provider: Who gives you access to the model
- API: Communication interface between programs
- API key: Secret credential that identifies you
- Ollama: Tool for running local models
- Quantization: Reducing model size for efficiency
- OpenRouter: Model and provider aggregator
- VRAM: GPU memory
Next week
With all this foundational knowledge, we’re ready for the big leap: AI agents. Not simple chatbots that answer questions, but programs that execute tasks, use tools, and work autonomously.
Week 4 shifts gears.
Key concepts today
- The complete flow: You → Interface → Framework → Provider → Model → Response
- Each layer is interchangeable: You can switch model, provider, or framework independently
- The choice depends on your use case: There’s no universal “best”
- Combination is key: Expensive model for quality + cheap model for volume
Next lesson: We start Week 4 — What is an AI agent? More than a chatbot.