A collection of guides and advanced techniques for crafting effective prompts to maximize the potential of Large Language Models (LLMs) like ChatGPT, Gemini and Claude.
Prompt version control means storing every prompt as a file in Git, together with the model and settings it runs with, testing each change against a fixed set of examples, and keeping a history you can roll back to. If you have ever dug through a “Prompts_Final_v3_OLD.docx” trying to work out why the AI behaved … Read more
Have you ever found yourself staring at a complex prompt, watching your AI model hallucinate or provide a surface-level answer that misses the mark? It’s a common frustration. To get better results, sometimes we need to stop pushing forward and instead take a collective breath. This is where Step-Back Prompting comes into play. Developed by … Read more
Have you ever asked an AI a question, only to receive a response so vague it felt like reading a generic horoscope? Usually, the culprit isn’t the AI’s lack of intelligence—it’s a lack of context. As a Senior SEO Content Writer, I’ve seen how precision in prompting separates professional-grade outputs from digital noise. To fix … Read more
When you prompt an AI, you usually focus on what you want to see. You ask for “a sunset,” “a professional tone,” or “a Python script.” But as AI models become more sophisticated in 2026, the secret to precision isn’t just in the request—it’s in the exclusion. I like to think of prompting like sculpting. … Read more
In the early days of search engines, matching words was enough. If you searched for “baking,” the engine looked for the word “baking.” Today, AI and LLMs have shifted the landscape toward Semantic Discovery. We no longer just look for words; we look for the “ghosts” of meaning hidden between the lines. If you are … Read more
If you have ever asked an AI to “act as a world-class lawyer” or “write like a weary detective,” you have dabbled in the basics of persona engineering. But beneath the surface of these simple requests lies a sophisticated mechanism called persona-driven logic. By assigning a specific identity to a Large Language Model (LLM), you … Read more
Efficient coding is rarely about the first draft; it is about the relentless pursuit of refinement. If you have ever asked an AI to “fix my code” and received a mediocre result, you likely hit a wall in prompt engineering. To bridge the gap between “working code” and “high-performance code,” you need to leverage Recursive … Read more
Generating a technical synopsis can often feel like trying to condense a thousand-page manual into a single, punchy paragraph without losing the vital “why” and “how.” If you’ve ever asked an AI to summarize a complex white paper only to receive a generic, fluffy response, you know the struggle. This is where Few-Shot Learning becomes … Read more
Chain of verification prompting (CoVe) is a four-step technique that makes a large language model fact-check its own draft: it writes an answer, plans verification questions about each factual claim, answers those questions independently, and then rewrites the answer using only what held up. The method comes from a 2023 Meta AI research paper, where … Read more
A prompt engineering taxonomy sorts prompting techniques by the job they do: setting context, shaping reasoning, constraining the output, and improving the prompt itself. I use the label Deliberate Prompting for working this way: every line in a prompt has a purpose, and you can say which layer it belongs to. Researchers have catalogued 58 … Read more