# daveebbelaar/ai-cookbook

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3,772 stars · 1,349 forks · Python · mit

## Links

- GitHub: https://github.com/daveebbelaar/ai-cookbook
- Homepage: https://www.youtube.com/@daveebbelaar
- awesome-repositories: https://awesome-repositories.com/repository/daveebbelaar-ai-cookbook.md

## Topics

`agents` `ai` `anthropic` `llm` `openai` `python`

## Description

This project is an AI engineering cookbook and tutorial suite providing step-by-step patterns for building production-ready artificial intelligence systems. It serves as an implementation guide and framework for integrating large language models into software applications.

The repository functions as a generative AI pattern library, offering curated code snippets and modular scripts to connect models to external data and tools. It provides a collection of practical examples and reusable implementation patterns designed to accelerate the development of AI features and prototypes.

The codebase covers a broad range of AI application development, from generative AI implementation and LLM integration workflows to environment reproducibility through curated dependency mapping. It uses a tutorial-driven approach that decomposes complex workflows into manageable code blocks and guided steps.

## Tags

### Artificial Intelligence & ML

- [LLM Application Development](https://awesome-repositories.com/f/artificial-intelligence-ml/generative-ai-resources/generative-ai/llm-application-development.md) — Provides a comprehensive framework and guided examples for building functional applications using large language models.
- [AI Engineering Cookbooks](https://awesome-repositories.com/f/artificial-intelligence-ml/ai-engineering-cookbooks.md) — Acts as a comprehensive set of step-by-step patterns and guides for building production-ready AI systems.
- [AI System Construction](https://awesome-repositories.com/f/artificial-intelligence-ml/ai-system-construction.md) — Provides curated code examples and tutorials for integrating AI capabilities into software projects. ([source](https://www.youtube.com/@daveebbelaar))
- [LLM Tooling Integrations](https://awesome-repositories.com/f/artificial-intelligence-ml/artificial-intelligence-tooling/language-model-integrations/llm-tooling-integrations.md) — Implements connectors and interfaces that allow language models to access external data and execute software tools.
- [Generative AI Integration Patterns](https://awesome-repositories.com/f/artificial-intelligence-ml/generative-ai-integration-patterns.md) — Provides code-centric examples and implementation patterns for embedding generative models into software applications.
- [LLM Integration Frameworks](https://awesome-repositories.com/f/artificial-intelligence-ml/llm-integration-frameworks.md) — Implements a modular framework for standardizing communication and dependency management with external LLMs.
- [LLM Integration Guides](https://awesome-repositories.com/f/artificial-intelligence-ml/llm-integration-guides.md) — Offers technical documentation and workflows for common large language model integration tasks.
- [AI Workflow Reproducibility](https://awesome-repositories.com/f/artificial-intelligence-ml/agentic-systems-frameworks/agent-orchestration-multi-agent/control-flow-and-workflows/ai-workflow-management/ai-workflow-reproducibility.md) — Uses curated dependency mappings to ensure consistent AI agent behavior and project execution across environments.
- [AI Prototype Development](https://awesome-repositories.com/f/artificial-intelligence-ml/artificial-intelligence-tooling/platforms-and-runtime-environments/ai-prototype-development.md) — Implements code patterns for rapidly validating AI concepts through modular proof-of-concept implementations.
- [Implementation Step Decomposition](https://awesome-repositories.com/f/artificial-intelligence-ml/implementation-step-decomposition.md) — Decomposes complex AI workflows into a structured sequence of manageable, independently testable code blocks.

### Education & Learning Resources

- [AI Engineering Tutorials](https://awesome-repositories.com/f/education-learning-resources/ai-engineering-tutorials.md) — Provides practical guides and guided workflows for constructing AI-powered applications and prototypes.
- [Application Development Guides](https://awesome-repositories.com/f/education-learning-resources/application-development-guides.md) — Offers tutorials and resources for building end-to-end applications powered by large language models.
- [Runnable Demonstrations](https://awesome-repositories.com/f/education-learning-resources/documentation-examples/runnable-demonstrations.md) — Provides a sequence of isolated, executable code samples to demonstrate theoretical AI concepts.
- [Learning Examples](https://awesome-repositories.com/f/education-learning-resources/learning-examples.md) — Uses concrete code snippets and runnable scripts as curated educational examples to illustrate AI concepts.
- [Tutorial-Driven Implementations](https://awesome-repositories.com/f/education-learning-resources/tutorial-driven-implementations.md) — Structures the codebase as a sequence of guided steps demonstrating the end-to-end construction of AI systems.

### Software Engineering & Architecture

- [Modularization Patterns](https://awesome-repositories.com/f/software-engineering-architecture/software-architecture/architectural-patterns/modular-decoupled-design/modularization-patterns.md) — Organizes implementation patterns into discrete, reusable units to manage the complexity of AI system integration.
- [Engineering Pattern Libraries](https://awesome-repositories.com/f/software-engineering-architecture/engineering-pattern-libraries.md) — Offers a library of proven implementation templates to accelerate the development and testing of AI features.

### Part of an Awesome List

- [Code and Examples](https://awesome-repositories.com/f/awesome-lists/ai/code-and-examples.md) — Provides practical implementations and sample applications demonstrating common AI integration patterns.