Building functional workflows requires connecting the various components of an LLM application. Although different programming languages could theoretically be utilized, Python has become the primary standard for LLM development. Its widespread adoption in this domain is due to several contributing factors that highlight its suitability.
Python boasts an unparalleled ecosystem of libraries and frameworks specifically designed for data manipulation, scientific computing, machine learning, and increasingly, LLM operations. This extensive collection significantly accelerates development:
transformers library offers easy access to thousands of pre-trained models, including many LLMs, directly within Python.This rich environment means developers often don't have to build fundamental components from scratch. Instead, they can assemble sophisticated applications by leveraging existing, well-tested tools. The large and active community surrounding these libraries also provides ample documentation, tutorials, and support.
Diagram illustrating Python's role connecting various components in an LLM workflow.
Python's syntax is often praised for its clarity and resemblance to plain English. This makes the code relatively easy to write, understand, and maintain, which is a significant advantage when dealing with the potentially complex logic of LLM workflows. Constructing prompts, parsing responses, managing conditional logic based on LLM output, and chaining multiple operations together can become intricate. Python's readability helps manage this complexity. Furthermore, its interpreted nature facilitates rapid prototyping and iteration, allowing developers to quickly experiment with different prompts, models, and workflow structures.
LLM applications rarely exist in isolation. They need to interact with various external systems, data sources, and APIs. Python excels as an integration language, often referred to as "glue code". It provides straightforward ways to:
This flexibility makes it practical to build end-to-end applications that incorporate LLMs as one component within a larger system.
Most major LLM providers (OpenAI, Anthropic, Google, Cohere, etc.) prioritize Python by offering official Software Development Kits (SDKs). These SDKs simplify the process of interacting with their APIs, handling details like authentication, request formatting, response parsing, and error handling. Using an official SDK is generally more convenient and less error-prone than making raw HTTP requests, although Python's libraries like requests make the latter approach entirely feasible if needed.
As LLMs are often used in conjunction with specific datasets (e.g., in RAG systems), effective data handling is essential. Python, with libraries like Pandas and the specialized data loaders and indexers found in LlamaIndex, provides powerful tools for ingesting, cleaning, transforming, and preparing data for use by LLMs. Tasks ranging from reading text files and web pages to calculating text embeddings for vector storage are well-supported within the Python ecosystem.
Python is the dominant language in the broader fields of data science and machine learning. Consequently, a substantial amount of research, tooling development, and practical implementation related to AI happens in Python first. Choosing Python for LLM development aligns your work with industry standards, makes it easier to find relevant resources and talent, and ensures access to the latest advancements in the field.
In summary, Python's extensive libraries, ease of use, integration strengths, strong API support, data handling capabilities, and status as the industry standard make it an exceptionally well-suited choice for developing, experimenting with, and deploying LLM workflows and applications. This course will heavily utilize Python and its ecosystem to build practical LLM-powered systems.
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transformers library, which enables access to many pre-trained models including LLMs for Python users.© 2026 ApX Machine LearningAI Ethics & Transparency•