Advanced Prompting and Context Engineering

Explain the Tree-of-Thoughts (ToT) framework and contrast its search-based approach with the linear path of Chain-of-Thought.

Implement a basic ToT pattern by prompting a model to generate and evaluate multiple reasoning paths.

Design self-consistency prompting strategies to improve the reliability of answers by sampling and aggregating multiple outputs.

Apply prompt chaining to decompose complex tasks into a sequence of simpler, interconnected subtasks.

Implement dynamic few-shot example selection using semantic similarity search on embeddings.

Design prompts that reliably produce structured outputs like JSON or XML, including error handling instructions.

Design robust prompts that anticipate and handle edge cases and ambiguous user inputs.

Measure prompt token efficiency and apply techniques like prompt compression to reduce cost and latency.