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Prompt Engineering & Tuning

Table of Contents

🧩 What This Covers
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I develop and tune prompts that guide large language models (LLMs) to deliver consistent, business-relevant outputs - whether for automation, analysis, or internal tools. It’s not just about getting a response - it’s about getting the right one, reliably.

🛠 Common Scenarios
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  • Your prompts produce inconsistent or unclear results
  • You’re trying to automate tasks but need better control over responses
  • You’re using LLMs internally but not sure how to guide them effectively
  • You want to reduce hallucinations and improve accuracy
  • You’re prototyping tools that need robust prompt logic under the hood

📌 What I Focus On
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  • Writing structured, context-aware prompts for clarity and control
  • Designing prompt templates for repeatability and scale
  • Using chain-of-thought, system-role, or few-shot techniques where appropriate
  • Embedding business logic directly into prompt flow
  • Iterating and testing for reliability across scenarios

🚀 Outcomes You Can Expect
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  • More predictable and useful LLM responses
  • Prompts you can trust to perform under variation
  • A clear strategy for integrating LLMs into business tools or workflows
  • Faster prototyping with fewer false starts