AI for Developer Workflows

Contrast the Transformer architecture with prior NLP models (e.g., RNNs) to explain its advantages for sequence processing.

Explain how tokenization converts text into numerical representations for LLM processing.

Describe the role of self-attention in enabling transformers to understand contextual relationships in text.

Describe the pre-training objectives and scale of data used to train foundational LLMs.

Describe the stages of Reinforcement Learning from Human Feedback (RLHF), including reward model training and policy optimization.

Identify key capabilities and limitations of current LLMs, including hallucinations, context window constraints, and reasoning boundaries.

Write effective zero-shot prompts with clear instructions, context, and constraints.

Apply few-shot prompting using well-chosen examples to guide model output for complex tasks.

Use chain-of-thought and other structured reasoning patterns to improve performance on multi-step problems.

Craft system prompts to define a consistent model behavior, persona, and output format.

Engineer prompts that reliably produce structured outputs such as JSON or XML.

Apply structured thinking frameworks (e.g., first-principles, SWOT) via prompting to analyze complex problems.

Diagnose and refine failing prompts by identifying ambiguity, missing context, or weak constraints.

Select appropriate prompting strategies for diverse tasks including summarization, extraction, classification, and generation.