Tag: prompt engineering
Deterministic Prompts: How to Reduce Variance in LLM Responses
Learn how to reduce LLM output variance using deterministic prompts, parameter tuning (temperature, top-p), and structural strategies for production stability.
Task-Specific Prompt Blueprints for Search, Summarization, and Q&A
Learn how to move from ad-hoc prompting to structured prompt blueprints for LLMs. Expert guides on search, summarization, and Q&A using CoT and JSON Schema.
Prompting for Accuracy in Generative AI: Constraints, Quotes, and Extractive Answers
Learn how to stop AI hallucinations with precise prompting strategies. We explore constraints, role-playing, and real-world case studies from biomedical research to boost reliability.
Chain-of-Thought Prompts for Reasoning Tasks in Large Language Models
Chain-of-thought prompting helps large language models solve complex reasoning tasks by breaking problems into steps. It works best on models over 100 billion parameters and requires no fine-tuning-just well-structured prompts.
Inclusive Prompt Design for Diverse Users of Large Language Models
Inclusive prompt design ensures large language models work for everyone-not just fluent English speakers. Learn how IPEM improves accuracy, reduces frustration, and expands access for diverse users across cultures, languages, and abilities.
Teaching LLMs to Say 'I Don’t Know': Uncertainty Prompts That Reduce Hallucination
Learn how to reduce LLM hallucinations by teaching models to say 'I don't know' using uncertainty prompts and structured training methods like US-Tuning - proven to cut false confidence by 67% in real-world applications.
Prompting as Programming: How Natural Language Became the Interface for LLMs
Natural language is now the primary way humans interact with AI. Prompt engineering turns simple text into powerful programs, replacing code for many tasks. Learn how it works, why it's changing development, and how to use it effectively.
Prompt Length vs Output Quality: The Hidden Cost of Too Much Context in LLMs
Longer prompts don't improve LLM output-they hurt it. Discover why 2,000 tokens is the sweet spot for accuracy, speed, and cost-efficiency, and how to fix bloated prompts today.
Prompt Compression: Cut Token Costs Without Losing LLM Accuracy
Prompt compression cuts LLM input costs by up to 80% without sacrificing answer quality. Learn how to reduce tokens using hard and soft methods, real-world savings, and when to avoid it.