Prompt Engineering and In-Context Learning
Explore prompt engineering as a programming paradigm shaping model inference, understand in-context learning mechanics, chain-of-thought prompting benefits, system prompts, prompt injection risks, and function calling. This lesson prepares you for AI engineering interviews by deepening your reasoning around how large language models interpret and execute prompts in applied AI systems.
We'll cover the following...
- What is in-context learning and why does it work?
- What is chain-of-thought prompting and when does it help?
- What is a system prompt and how does it work architecturally?
- What is prompt injection and how is it mitigated?
- What is function calling and how does it work?
- How would you implement a few-shot prompt builder and tool-call parser?
- What’s next?
Prompting is often dismissed as just writing instructions, which is exactly the wrong mental model. Prompting is a programming paradigm. You are writing programs that run on a stochastic interpreter whose behavior is shaped by training, not by a spec. Understanding how and why prompts work, how they fail, and how attackers exploit them is essential for every applied AI role. In interviews, prompting questions are actually testing your mental model of how LLMs work at inference time.
In-context learning is not the model “learning” in the gradient descent sense. No weights are updated. What the model is doing is closer to implicit pattern matching: the examples in the prompt shift the model’s predictive distribution toward a specific task format and output style. This distinction matters for understanding both the capabilities and the limitations of ICL.
What is in-context learning and why does it work?
In-context learning (ICL) is the ability of a model to perform a new task when given examples of the task directly in the prompt, without any gradient updates or fine-tuning. Zero-shot ICL gives no examples; the model relies entirely on instruction following. Few-shot ICL provides k demonstrations (prompt-completion pairs), typically 3-8.
The empirical behavior of ICL has a ...