prompt-engineering

To write better LLM prompts, apply prompt engineering techniques like adding few-shot examples, implementing chain-of-thought reasoning, and optimizing prompt structure for token efficiency. This approach improves LLM clarity, conciseness, and output accuracy.

Designing RAG systems effectively requires applying specific RAG design principles and prompt structures. This ensures the retrieval-augmented generation system provides consistent, accurate answers by optimizing how context is integrated with LLM prompts.

Chain of thought prompting works by guiding an LLM through intermediate reasoning steps before generating a final answer. This pattern helps large language models solve complex problems more accurately by structuring their thought process.

Yes, you can use ReAct patterns for building autonomous agent workflows. ReAct integrates reasoning and acting within LLM agents, allowing them to process information, decide on actions, and execute complex workflows autonomously.

Your LLM prompt returns inconsistent answers due to suboptimal prompt structure lacking few-shot examples or clear constraints. Prompt optimization techniques can rephrase instructions to achieve consistent, high-quality document summarization results.