Interactive Jupyter Notebook Examples For Hands On Prompt Engineering Practice

via “interactive-prompt-design-and-testing”

Google's prototyping IDE for Gemini models.

Unique: Integrated multimodal input handling (images, video, text) directly in the browser UI without requiring separate API calls or file uploads to external storage — images are embedded in the conversation context client-side

vs others: Faster than OpenAI Playground for multimodal testing because it natively supports image/video input in the chat interface rather than requiring separate file management steps

via “interactive-prompt-engineering-and-testing-lab”

IBM enterprise AI platform — Granite models, prompt lab, tuning, governance, compliance.

Unique: Combines interactive prompt testing with real-time parameter tuning and side-by-side comparison in a unified web interface, allowing non-technical users to optimize prompts without touching code or APIs — most competitors (OpenAI Playground, Anthropic Console) offer similar UIs but watsonx.ai integrates this with enterprise governance and audit trails

vs others: Integrated with enterprise governance tooling (audit trails, bias detection) whereas OpenAI Playground and Anthropic Console are consumer-focused with minimal compliance features

via “jupyter-notebook-based-interactive-agent-development”

50+ tutorials and implementations for Generative AI Agent techniques, from basic conversational bots to complex multi-agent systems.

Unique: Organizes all 45+ agent implementations as self-contained, executable Jupyter notebooks with clear explanations and step-by-step execution. This approach prioritizes learning and experimentation over production deployment, making the repository highly accessible to developers new to agent development.

vs others: Provides interactive, executable learning materials that enable rapid experimentation, whereas traditional documentation or code repositories require setup and may be harder to follow. Notebooks also serve as templates for building new agents.

via “jupyterlab-interactive-notebook-interface”

All-in-One Sandbox for AI Agents that combines Browser, Shell, File, MCP and VSCode Server in a single Docker container.

Unique: Provides JupyterLab interface within the sandbox container with direct access to the shared /home/gem file system and stateful Jupyter kernel, enabling interactive notebook-based agent development without external notebook servers. Unlike cloud-based Jupyter services, notebooks have zero-latency access to sandbox execution endpoints.

vs others: More integrated than external Jupyter services because notebooks can directly access files created by browser automation and shell commands; more interactive than batch processing because developers can inspect kernel state and adjust analysis in real-time.

via “prompt optimization through iterative refinement”

22 prompt engineering techniques with hands-on Jupyter Notebook tutorials, from fundamental concepts to advanced strategies for leveraging LLMs.

Unique: Provides Jupyter notebooks showing systematic prompt optimization with measurement frameworks, A/B testing patterns, and iteration strategies. Includes code for comparing prompt variations and tracking improvements across iterations, rather than treating optimization as ad-hoc trial-and-error.

vs others: More rigorous than casual prompt tweaking because it teaches measurement-driven optimization with explicit test cases and metrics, whereas most guides rely on subjective judgment.

via “interactive model playground with multi-modal input”

Build AI agents and workflows in Microsoft Foundry, experiment with open or proprietary models.

Unique: Embeds a full-featured chat playground directly in VS Code sidebar with streaming response visualization and parameter controls, avoiding the need to switch to web-based model playgrounds (OpenAI Playground, Claude Console) or separate tools

vs others: Keeps prompt iteration in the development environment with instant feedback and parameter tuning, reducing context-switching compared to web-based playgrounds or API-only workflows

via “hands-on code implementation with jupyter notebooks”

📚 从零开始构建大模型

Unique: Delivers all content as executable Jupyter notebooks with integrated theory and code, allowing learners to run examples immediately and modify code to experiment, rather than providing separate documentation and code repositories

vs others: More interactive than reading documentation because learners can execute code, modify parameters, and see results immediately without setting up separate development environments

via “interactive notebook-based image generation with parameter exploration”

[CVPR 2025 Oral]Infinity ∞ : Scaling Bitwise AutoRegressive Modeling for High-Resolution Image Synthesis

Unique: Provides pre-configured notebooks with integrated visualization and parameter controls, eliminating setup overhead for users unfamiliar with the codebase. Notebooks include helper functions for batch generation and quality visualization.

vs others: Lower barrier to entry compared to command-line tools; enables non-technical users to explore model capabilities without scripting knowledge.

via “prompt templates for notebook-specific ai tasks”

🪐 🔧 Model Context Protocol (MCP) Server for Jupyter.

Unique: Provides MCP-native prompt templates that guide AI clients in notebook-specific tasks, reducing the need for clients to construct prompts from scratch and standardizing AI behavior across teams.

vs others: Offers structured task guidance that generic AI clients lack, enabling consistent and high-quality AI interactions with notebooks without requiring client-side prompt engineering.

via “interactive jupyter notebook examples for hands-on prompt engineering practice”

🐙 Guides, papers, lessons, notebooks and resources for prompt engineering, context engineering, RAG, and AI Agents.

Unique: Provides executable notebooks integrated within the documentation platform, enabling learners to run examples directly from the guide without setting up separate environments

vs others: More interactive than static documentation because code is executable; more accessible than academic papers because it includes working examples; more practical than tutorials because learners can modify and experiment

via “prompt engineering with structured instruction design”

本项目是一个面向小白开发者的大模型应用开发教程，在线阅读地址：https://datawhalechina.github.io/llm-universe/

Unique: Provides executable prompt engineering examples showing before/after comparisons of instruction quality, demonstrating how specific design choices (role definition, context framing, output format) improve response quality; includes Chinese language prompt examples for non-English applications

vs others: More practical than theoretical prompt engineering papers because it shows runnable examples; more comprehensive than single-technique tutorials because it covers multiple instruction patterns; more accessible than research papers because it uses beginner-friendly language and Jupyter notebooks

via “educational content and interactive learning with kids learning game”

f.k.a. Awesome ChatGPT Prompts. Share, discover, and collect prompts from the community. Free and open source — self-host for your organization with complete privacy.

Unique: Integrates educational content and gamification into the prompt library platform, treating prompt engineering as a learnable skill with structured curriculum and interactive exercises. The kids game is a unique differentiator that makes AI concepts accessible to younger audiences.

vs others: More engaging than static documentation because it includes interactive exercises and gamification; more accessible than academic courses because it's free and integrated into the platform. Differs from generic learning platforms by being specialized for prompt engineering.

via “jupyter notebook integration with in-cell experiment execution and result inspection”

Tools for LLM prompt testing and experimentation

Unique: Provides first-class Jupyter integration through IPython display hooks and in-cell execution, allowing experiments to be run and results inspected without leaving the notebook, with automatic rendering of tables and plots in cell outputs

vs others: More integrated than tools requiring external execution environments; enables faster iteration than command-line tools while maintaining full programmatic access to results

via “prompt engineering application use-case library”

Guide and resources for prompt engineering.

via “interactive notebooks for hands-on learning”

Examples and guides for using the OpenAI API.

Unique: The integration of live code execution with educational content sets this Cookbook apart, allowing for a more engaging learning process compared to static documentation.

vs others: Provides a more immersive and interactive learning experience than traditional tutorials or documentation.

via “prompt engineering technique instruction with interactive examples”

Anthropic's educational courses.

Unique: Combines theoretical prompt engineering principles with executable Jupyter notebooks that learners run against live Claude API, creating immediate feedback loops where prompt modifications produce observable output changes. Organized as a progressive curriculum where each technique builds on prior knowledge rather than standalone reference material.

vs others: More hands-on and structured than blog posts or documentation because learners execute real prompts and observe results directly, and more comprehensive than single-technique tutorials because it covers the full spectrum of core techniques in a coherent learning sequence

via “interactive notebook-based experimentation environment”

The in-person certificate courses are not free, but all of the content is available on Fast.ai as MOOCs.

via “interactive prompt engineering sandbox with model comparison”


Unique: Integrates multi-model comparison directly into the learning environment without requiring learners to manage separate API clients or authentication. Uses SageMaker's model hosting to enable low-latency local model testing (e.g., Llama 2) alongside cloud-hosted proprietary models, reducing the friction between learning and production deployment.

vs others: More integrated than standalone prompt testing tools (like Promptfoo) because it's embedded in the curriculum with guided exercises, but less feature-rich than specialized prompt management platforms because it prioritizes simplicity for learners over advanced versioning and team collaboration.

via “interactive prompt crafting”

A free, open source course on communicating with artificial intelligence.

Unique: Utilizes an interactive, modular learning system that allows for real-time prompt testing and feedback, unlike static tutorials.

vs others: More engaging than traditional text-based tutorials, as it offers hands-on practice with instant feedback.

via “interactive learning resources and notebook-based tutorials”