This blog was originally published on IBM Developer.

Python is one of the most popular programming languages in the generative AI landscape. Whether you're implementing RAG pipelines, optimizing AI models, or taking advantage of AI agent frameworks, Python is front and center due to it's simplicity and versatility.

If you're ready to start building with Python, check out this curated list of our top 5 blogs, articles, and tutorials so far this year. They'll help you enhance your Python skills, stay ahead of the curve, and get inspired for your next generative AI project!

#5 Enhancing RAG performance with smart chunking strategies

In Enhancing RAG performance with smart chunking strategies, you learn just how important chunking is when retrieving data in your RAG system. This article explains how you first need to understand the specific nature of your data and then select the right chunking strategy to improve the performance of your RAG system.

Explore more content on IBM Developer about the retrieval-augmented generation (RAG).

#4 Cache augmented generation (CAG): Enhancing speed and efficiency in AI systems

The article Cache augmented generation (CAG): Enhancing speed and efficiency in AI systems explains what cache augmented generation (CAG) is, how it works, and its potential impact on AI systems.

Explore more content on IBM Developer about the retrieval-augmented generation (RAG).

#3 Optimizing LLMs with cache augmented generation

In Optimizing LLMs with cache augmented generation, you learn how cache augmented generation (CAG) and its integration with the Granite models helps to optimize LLM workflows by preloading knowledge and precomputing states, making it ideal for static data set applications with fast response needs.

Explore more content on IBM Developer about large language models (LLMs) and the Granite models.

#2 Comparing AI agent frameworks: CrewAI, LangGraph, and BeeAI

Comparing AI agent frameworks: CrewAI, LangGraph, and BeeAI explains the capabilities, features, and implementation considerations of leading AI agent frameworks focused on multi-agent collaboration and orchestration. The article shows how CrewAI, LangGraph, and BeeAI each offer powerful capabilities for implementing AI agent systems, with distinct advantages based on specific requirements and use cases.

Check out an additional article, Implementing AI agents with AI agent frameworks, for a hands-on look at implementing an AI agent in each of these three AI agent frameworks.

Explore more content on IBM Developer about agentic AI.

#1 Create a LangChain AI Agent in Python using watsonx

In the tutorial Create a LangChain AI Agent in Python using watsonx, you learn how to use the LangChain Python package to build an AI agent that uses its custom tools to return a URL directing to NASA's Astronomy Picture of the Day.

Explore more content on IBM Developer about agentic AI.

Want more?

Python is a simple and versatile open source programming language that's ideal for AI tasks. Now that you've seen what developers were looking to learn about Python in the first half of 2025, make sure you stay tuned to see what the rest of 2025 brings. We'll bring you the latest in Python concepts, step-by-step tutorials, and AI techniques!

Check out more of our Python content on our Python hub page.