Context & In-Context Learning
Autoregressive generation, the prompt stack, context engineering, and long-context degradation.
6concepts
28flashcards
46minutes of reading
- 01 Context Engineering The discipline of curating exactly which tokens occupy the model's window during inference, and why it became the core skill for building agents that run longer than a single turn.
- 02 In-Context Learning How large models learn a task from examples in the prompt alone, with no weight updates, and why this emergent ability reframed how we use LLMs.
- 03 The Prompt Stack and Chat Roles What a chat prompt actually is under the hood: a single token sequence built from system, user, and assistant turns wrapped in special tokens, and why that structure is load-bearing.