Tag: prompt engineering
Customizing LLMs: Fine-Tuning, Adapters (LoRA), and Prompts Explained
Explore LLM customization paths: full fine-tuning, LoRA adapters, and prompt engineering. Learn which method fits your budget, compute limits, and task needs for optimal AI performance.
How to Make LLMs Self-Correct: Error Messages and Feedback Prompts That Work
Learn how to use error messages and feedback prompts to enable LLM self-correction. Discover intrinsic, multi-turn, and FTR methods to reduce AI errors by up to 45%.
Playbooks for RAG, Agents, and Prompt Engineering at Scale
Learn how to build scalable AI systems using proven playbooks for RAG, agents, and prompt engineering. Discover strategies for separating prompts from knowledge bases, optimizing retrieval pipelines, and managing operational costs effectively.
Chain-of-Thought in Vibe Coding: Why Explanations Beat Code First
Learn how Chain-of-Thought prompting transforms vibe coding by forcing AI to explain reasoning before writing code, reducing bugs and improving reliability.
Critique-and-Revise Prompting: How to Build Iterative Refinement Loops for AI
Master critique-and-revise prompting to turn AI drafts into polished, professional outputs using iterative refinement loops and self-correction techniques.
Long-Context Prompt Design: How to Position Information for LLM Attention
Learn how to optimize LLM performance by mastering long-context prompt design. Discover the "Lost in the Middle" phenomenon and strategies to position critical info for maximum attention.
Vibe Coding vs AI Pair Programming: Choosing the Right AI Workflow
Discover the difference between Vibe Coding and AI Pair Programming. Learn when to prioritize speed with vibe coding and when to ensure quality with AI pair programming.
Debugging Prompts: Systematic Methods to Improve LLM Outputs
Learn systematic methods to debug and improve LLM outputs, from task decomposition and RAG to advanced mathematical steering and prompt chaining.
Stop Sequences in Large Language Models: Preventing Runaway Generations
Stop sequences are a simple but powerful tool to prevent AI models from overgenerating text. They improve accuracy, cut costs, and ensure clean outputs - essential for any real-world AI application.
Few-Shot Prompting Strategies That Boost LLM Accuracy and Consistency
Few-shot prompting boosts LLM accuracy by 15-40% using just 2-8 examples. Learn how to choose the right examples, avoid over-prompting, and combine it with chain-of-thought for better results - without fine-tuning.
In-Context Learning Explained: How LLMs Learn from Prompts Without Training
In-Context Learning allows LLMs to adapt to new tasks using examples in prompts-no retraining needed. Discover how it works, its benefits, limitations, and real-world applications in AI today.
Few-Shot vs Fine-Tuned Generative AI: How Product Teams Should Choose
Product teams need to choose between few-shot learning and fine-tuning for generative AI. This guide breaks down when to use each based on data, cost, complexity, and speed - with real-world examples and clear decision criteria.
