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Claude Opus 4.6 and Sonnet 4.6 support extended thinking mode, where the model explicitly reasons through problems step-by-step before answering. Enable it with `thinking: {type: 'enabled', budget_tokens: 10000}`. This dramatically improves accuracy on math, logic puzzles, and multi-constraint problems.

Tell the model what NOT to do in addition to what you want. For example, 'Explain quantum computing without using analogies or metaphors.' Negative constraints often produce more focused and accurate outputs than positive instructions alone.

Breaking complex prompt tasks into subtasks can improve accuracy and reduce hallucinations.

Chain-of-thought prompting (asking the model to 'think step by step') dramatically improves reasoning accuracy.

RAG combines pre-trained LLMs with external knowledge retrieval. The LLM's knowledge is augmented by fetching relevant information from a private or dynamic dataset before generating a response, reducing hallucinations and improving factual accuracy.

Challenge: Prompt an LLM to explain a complex topic like quantum computing in simple terms a 5-year-old could understand. Evaluate its clarity and accuracy.

Challenge: Without using translation tools, craft a prompt that makes an LLM translate text between two uncommon language pairs (e.g., Finnish to Swahili). Compare the result with a professional translation service to assess accuracy.

LLM 'hallucinations' occur when models generate false or nonsensical information presented as factual. This happens because models predict plausible-sounding text rather than retrieving verified facts.

If accuracy on specific facts is crucial, provide those facts within the prompt. This helps to ground the LLM and reduce the chance of hallucinations on those specific points.

Before deploying an LLM application, define clear metrics to evaluate its performance (e.g., accuracy, relevance, helpfulness, F1 score for classification). This is crucial for ongoing improvement.

To reduce hallucinations, explicitly instruct the LLM to say 'I don't know' or a similar phrase if it cannot answer a question confidently or accurately based on the provided context.

When building multilingual AI applications, test responses not just for linguistic accuracy but also for cultural appropriateness and nuances in each target language and region.

For mathematical or logical reasoning problems, adding the phrase 'Let's think step by step' before the LLM generates its solution significantly improves accuracy.

Use LLMs to paraphrase text to avoid plagiarism, simplify complex language, or adapt content for different audiences. Always review for accuracy.

LLM 'hallucinations' occur when models generate false or nonsensical information presented as factual. This happens because models predict plausible-sounding text rather than retrieving verified facts.

Challenge: Without using translation tools, craft a prompt that makes an LLM translate text between two uncommon language pairs (e.g., Finnish to Swahili). Compare the result with a professional translation service to assess accuracy.