Prompt Engineering Guide: 2026 Edition (Steal My System) Want a single framework that works across ChatGPT (GPT-5/4.1), Claude 4, Google Gemini, Perplexity, and even reasoning models (O3 / O4-mini)? This episode distills hundreds of hours of testing + the latest docs into a reusable prompt engineering system you can apply to any large language model. We’ll cover the core template (Role → Task → Context → Examples → Output → Constraints → Instructions), then layer on advanced techniques like Chain of Verification (CoV) and Reverse Prompting, and finish with how context engineering (RAG, memory, connectors) complements great prompts. What you will learn - The 2026 Prompt Framework that travels cleanly across models (and what to tweak per model). - Model-specific rules: when to add step-by-step guidance vs. when it hurts (reasoning models). - Chain of Verification to reduce hallucinations and force evidence-backed answers. - Reverse Prompting to let the model craft (and run) the optimal prompt for your goal. - Context Engineering vs Prompt Engineering - how RAG, memory, and external data supercharge results. - Per-model tips for GPT-5, GPT-4.1, Claude 4, Gemini 2.5, O3/O4-mini, Perplexity (search-centric). Timestamps 00:00 Introduction 00:49 The Prompting Framework 09:35 Chain of Verification 10:50 Reverse Prompting 12:17 Prompt Engineering vs. Context Engineering Key takeaways (cheat sheet) - Standard models (GPT-5/4.1, Claude, Gemini): ask for step-by-step thinking, state uncertainty over guessing, use few-shot examples for tone/format. - Reasoning models (O3/O4-mini): don’t force chain-of-thought; keep prompts lean; minimize context. - Perplexity: treat as retrieval-augmented generation, avoid few-shot examples in the initial prompt; use CoV as a follow-up. - Output control: specify format, length, and structure precisely (tables, sections, word counts). - Constraints: crisp, measurable rules outperform vague ones. Resources - Framework Explanation - Plug-and-Play Framework Who this is for Analysts, operators, PMs, consultants, and creators who want reliable, repeatable outputs—not one-off prompt hacks. Perfect if you’re comparing prompt engineering courses or want a faster path to learn prompt engineering in practice. If this helps, consider subscribing. I post weekly, no-fluff tutorials that turn AI & finance into your personal advantage. Search helpers: prompt engineering, what is prompt engineering, learn prompt engineering, prompt engineering guide 2026, prompt engineering course, LLM prompting, ChatGPT tricks, Claude skills, Gemini prompts, RAG, context engineering, large language models.