LLM Prompting Best Practices for Production Reliability

This article explains common pitfalls encountered when using Large Language Models (LLMs) in production applications and offers solutions. It details how LLMs' stochastic nature, controlled by 'temperature' settings, can lead to hallucinations even with low temperatures. The piece also covers Chain of Thought prompting techniques, including Zero-Shot and Few-Shot CoT, and emphasizes the importance of structured outputs using JSON schemas to prevent errors. Finally, it outlines five key prompt failure modes such as prompt injection, 'lost in the middle' issues, conflicting rules, the 'pink elephant' problem, and sycophancy, providing strategies to mitigate each. AI

IMPACT Provides actionable strategies for developers to improve the reliability and robustness of LLM-powered applications.

RANK_REASON Article provides practical advice and techniques for using LLMs in software development.

Read on dev.to — LLM tag →

• Applied AI Engineering
• .chatgpt
• Few-Shot CoT
• JSON
• LLM
• prompt injection
• temperature
• Zero-Shot CoT

• product
• other

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →