Prompt Engineering

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What It Is:

Prompt engineering is the structured design of inputs that optimize how Large Language Models (LLMs) like GPT-4/5, Claude, and Gemini respond.

Technical Practices:
       •        Few-shot learning: Embedding examples in the prompt to guide model output.
       •        Chain-of-thought prompting: Forcing the model to “think step by step,” improving reasoning accuracy.
       •        Role/Context framing: Assigning the AI an identity (e.g., “You are a financial analyst…”) to control tone and domain.
       •        Guardrails: Using prompt templates with restrictions to reduce hallucination and enforce compliance.

Advanced Techniques:
       •        Programmatic prompting: Automated pipelines that generate and refine prompts at scale.
       •        Retrieval-Augmented Generation (RAG): Combining prompts with external data sources for grounded responses.
       •        Evaluation frameworks: Using metrics like BLEU, ROUGE, or embedding similarity to assess prompt effectiveness.

Applications: Customer service, legal drafting, content creation, coding copilots.

If AI is a genius polyglot, prompt engineering is learning the dialect that makes it listen best.

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