Launching LLMs Fundamentals
With LLMs multiplying fast, our new Fundamentals module helps you master model selection, tradeoffs, and coding workflows.
Over the past year, the number of available LLMs has exploded. From Claude 4 Sonnet and GPT-5 to open-source models running locally, developers now face more choices than ever but understanding how to use each of them and evaluating the tasks they perform best represents a challenge. That’s why we built Module 2: LLM Fundamentals for AI Coding University to serve as the foundation for understanding how LLMS work and how to interact with them for specific tasks and goals.
🔑 What You’ll Learn
- Model selection and providers – Tradeoffs between running models locally, using APIs, or routing through aggregators and how latency, benchmarks, and reliability shape your workflow.
- How architecture impacts coding tasks – Why some models excel at code generation while others are better for other coding tasks.
- The economics of AI development – Balancing cost, speed, and quality. When running models on your own hardware makes sense (and when it doesn’t).
⚡ Why This Matters in Cline
Because of Cline's model-agnostic by design, everything you learn in the module is immediately actionable:
- Compare different models on the same task with a few clicks.
- Test models locally through LMStudio or Ollama.
- Experiment with different providers to optimize for speed or reliability.
Ready to Dive In?
The LLMs Fundamentals module is live now. Whether you’re just starting your AI journey or ready to level up your workflow, it will help you think about the different characteristics to consider for coding with LLMs.
