Master AI Prompt crafting for LLMs in 2026

Explore prompt engineering as an iterative process of crafting inputs, testing responses, and refining prompts to get the best results, while acknowledging its non-deterministic nature and evolving context engineering.

Master in-context learning and few-shot prompting by conditioning the model with rich context and examples, using system prompts and K-shot prompting to guide responses.

Master chain-of-thought prompting by breaking problems into smaller steps and guiding the model to think step by step, improving performance across documents, images, data, and calculations.

Explore a wide range of prompting techniques, learn what each technique does from recent research, and identify product management use cases. Apply these techniques in IPL programs and real-world activities.

Understand how bad prompts hinder using generative AI, and develop clear prompts with data references through authentic prompt crafting, avoiding amateur shortcuts.

Explore a comprehensive AI prompt library for product managers by the IPL team, featuring 200+ ready-made prompts across 20+ areas, designed as starter prompts to adapt with prompt crafting methods.

Treat prompts like recipes: identify ingredients, outline instructions, and iterate with experiments, as the biryani analogy shows refining outputs through trials.

Start with a simple prompt and build it using design principles and components. Craft context within each element, apply prompt instructions, and iterate through refinements to maximize prompt effectiveness.

Define the coverage of a two-hour masterclass with a simple prompt to avoid garbage in, garbage out, guiding audience-focused outcomes for a workshop.

Explore the co-created prompt design framework co-star, guiding context, objective, style, tone, audience, response, and steps, supported by a 2000-prompt library to teach you how to fish.

Define context to bound a model’s scope with background on you, your task, and objectives; adopt an interactive prompting approach that asks the model for readiness before proceeding.

Define the objective to focus the large language model's vocabulary on task-specific words, clarifying the outcome, purpose, and goal, and then outline the steps to achieve it.

Explore how adopting a specific persona—from Shakespeare to a product manager or a shopper—steers prompts and makes the model behave like that figure.

Explore how to command a model's response by selecting and switching tones—from instructional to humorous, witty, or inspirational—and see the entire response adapt to the chosen tone.

Craft prompts to tailor model responses to your audience, adjusting language and explanations—from expert to five-year-old level—so outputs align with the audience's understanding.

Design prompts step by step using chain of thought prompting, k-shot prompting, and multi-persona prompting to guide large language models.

Structure your prompt by sectioning it into paragraphs and sections for design, context, objective, and style, using hashtags as markers to improve readability and embeddings.

Learn how language shapes AI prompts by avoiding unnecessary politeness, using direct, instructive phrasing, and experimenting with incentives to improve model performance and token efficiency.

Master interaction with large language models by asking the model what to write when you’re unsure, and craft prompts with deep, page-length detail.

Tailor prompts to your audience, your persona, the structure, and the style to achieve clarity. Learn to explain content clearly by aligning it with the audience, persona, structure, and style.

Explore k-shot, zero-shot, one-shot, and n-shot prompting, and see how one or two examples guide models. Apply a simple prompt to customer reviews to generate actionable feature ideas.

Learn to convert time-consuming prompt crafting into reusable templates by externalizing input variables with XML-style tags, enabling reuse across contexts and models.