Structuring Language Model Interactions with APIs and DSPy

Course details

This comprehensive course teaches developers how to build robust AI applications using DSPy, a powerful framework that transforms traditional prompt engineering into structured programming. Through hands-on examples, learn to create clean boundary APIs that separate concerns, making your AI systems more maintainable, testable, and scalable. Instructor Harit Himanshu shows you how to implement a multi-hop question answering system that retrieves information, reasons across multiple sources, and generates accurate responses to complex questions. Check out this course to learn the skills to design modular AI architectures with clear interfaces, applying software engineering best practices to language model applications.

Learning objectives
Define and implement clean boundary APIs for language model interactions using DSPy, transforming brittle prompt engineering into structured, maintainable code.
Design modular AI systems with clear separation of concerns by creating purpose-specific DSPy Signatures and Modules for each component.
Implement a complete multi-hop reasoning system that retrieves information, performs step-by-step analysis, and generates accurate responses to complex questions.
Apply software engineering best practices to AI application development, including component testing, interface design, and system composition.
Leverage DSPy abstraction capabilities to create AI applications that can be easily maintained, extended, and optimized over time.

Concepts

0. Introduction

01 - Structuring language model interactions with APIs and DSPy
02 - What you should know

1. Introduction to DSPy and Boundary APIs

03 - Understanding boundary APIs in AI systems
04 - Traditional prompting vs. DSPy programming
05 - Course project overview
06 - Accessing the course code

2. Setting Up the Environment in VSCode

07 - Installing dependencies
08 - Configuring VSCode with Jupyter
09 - Setting up API keys
10 - Testing DSPy LLM configuration

3. Working with DSPy Signatures

11 - Understanding signatures
12 - Working with inline signatures
13 - Working with class-based signatures
14 - Inspecting DSPy history
15 - Understanding LLM usage with DSPy

4. Working with DSPy Modules

16 - Understanding modules
17 - Creating custom modules

5. Building the RAG Component

18 - Understanding retrieval-augmented generation
19 - Working with the ChromaDB retriever module
20 - Working with the ColBERTv2 retriever module
21 - Developing a multi-hop reasoning RAG pipeline

6. Composing a Production Pipeline

22 - Understanding the production pipeline workflow
23 - Working with evaluation workflow
24 - Working with optimization workflow
25 - Serving an optimized model with FastAPI

Conclusion

26 - Next steps