prompt-engineering-specialist

To optimize LLM prompts for reduced token usage and latency, apply advanced techniques like Chain-of-Thought and Few-Shot learning to structure the prompt, maintaining response quality while minimizing processing overhead.

The best way to extract structured JSON from unstructured text is by designing prompts that enforce strict schema adherence and handle edge cases, ensuring the LLM reliably outputs the desired data format.

Chain-of-Thought prompting mitigates hallucinations by guiding the LLM through explicit reasoning steps before generating the final output, which improves response accuracy and reduces fabricated information.

Yes, you can use Few-Shot learning for sentiment analysis by providing the LLM with several labeled examples within the prompt, enabling it to accurately classify the sentiment of new customer reviews.

You need ReAct prompting when designing complex agentic workflows that require the LLM to reason about a task and take actions dynamically, integrating thought processes with external tool usage.