Asking Questions

Prompt engineering is a rapidly evolving skill. It bridges human intent and AI capability, requiring understanding of how language models process input.

Asking AI for JSON, XML, or specific formats enables programmatic use. 'Return a JSON object with keys: name, description, category' works reliably.

For complex tasks, break prompts into numbered steps. The AI follows the sequence, producing organized output for each step.

Ask AI to generate multiple solutions, then identify the most common answer. Self-consistency improves accuracy on uncertain problems.

Detailed persona descriptions control AI's writing style, vocabulary level, and tone. This is essential for content creation and educational applications.

Templates with placeholder variables create reusable prompts. 'Analyze {text} for {criteria} and output in {format}' scales to many inputs.

Measure prompts against criteria: accuracy, consistency, relevance, and efficiency. A/B testing different prompts reveals what works best.

Different fields require specialized prompting techniques. Legal prompts need precision; creative prompts need openness; technical prompts need structure.

Code-focused prompts work best with clear specifications: language, function name, inputs, outputs, and edge cases. AI generates code you then review.

Prompting cannot make AI do things outside its training. It cannot access real-time data (unless connected), perform true reasoning, or guarantee accuracy.

Collect and organize effective prompts for reuse. A personal prompt library saves time and consistently produces good results.

LLMs tokenize prompts, embed tokens as vectors, and process them through transformer layers. The prompt's structure directly affects attention patterns.

Effective prompting combines technique, understanding of models, and iteration. From CoT to injection defense, prompting is both art and engineering.

System prompts set the AI's behavior for an entire conversation — defining its role, rules, and personality. They are the foundation of AI applications.

Zero-shot gives no examples; few-shot provides 2-5 examples. Few-shot dramatically improves performance on structured tasks like classification.

Adding 'think step by step' makes AI show its reasoning, which improves accuracy on math and logic problems. CoT prompting mimics human reasoning.

ToT prompting has the AI explore multiple reasoning paths simultaneously, evaluate them, and choose the best one. It solves harder problems than CoT.

Prompt injection tricks AI into ignoring its instructions. 'Ignore previous instructions and...' is a common attack. Defense requires input sanitization.

Temperature controls randomness in outputs. Low temperature (0.1) gives deterministic answers. High temperature (0.9) gives creative, varied responses.

Prompts consume tokens from the context window. Efficient prompts minimize token usage while maximizing information, balancing brevity with clarity.