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Assign a specific expert role to the model (e.g., 'You are a senior database architect with 20 years of PostgreSQL experience'). Role prompting activates domain-specific knowledge patterns and produces more authoritative, detailed responses than generic prompts.

Challenge: Find a complex technical article (e.g., from arXiv). Ask an LLM to summarize it for three different audiences: a 10-year-old child, a university undergraduate, and a PhD-level expert in the field. Compare the differences in language, detail, and focus.

Mixture of Experts (MoE) is an architecture where instead of one large neural network, the model consists of many smaller 'expert' networks. For any given input, a routing mechanism selects a small subset of these experts to process it. This allows models to scale up their parameter count dramatically while keeping computational costs manageable.

Start your prompt by telling the model to act as an expert persona. For example, 'Act as a senior copywriter specializing in luxury brands.' This primes the model to adopt the desired tone, style, and domain knowledge, leading to higher-quality outputs.

When generating content, tell the LLM who the target audience is (e.g., 'Explain this to a 5-year-old,' or 'Write this for an expert audience.'). This helps tailor the complexity and tone.

Start your prompt with 'Assume the role of a [expert/character].' This helps the LLM adopt the desired persona and knowledge base more effectively.

Ask LLMs to review code for potential security vulnerabilities, but always follow up with human security experts for critical applications.