FastAPI for GenAI Applications
Once a generative AI script grows from a personal experiment into something other people or systems need to call, it needs an actual API — a defined, reliable interface other code can talk to. FastAPI has become one of the most popular choices for building that layer in...
Common Python Errors in AI Projects
AI code breaks in predictable ways. Here are the traps that waste hours, with the fixes that save them. 1. Mutable Default Arguments # WRONG: All calls share the same list def predict(inputs, cache=[]): cache.append(inputs) return model(inputs) # RIGHT: Fresh list every call...
Python Project Structure for GenAI
A messy GenAI project becomes unmaintainable fast. Prompts scattered in notebooks, API keys hardcoded, and no separation between inference logic and business code. Here's a structure that scales. The Layout genai-app/ ├── src/ │ ├── __init__.py │ ├── config.py # Centralized...
Python Logging for AI Applications
AI systems fail in ways print statements can't capture — model drift, API timeouts, silent NaNs. Here's the minimal setup that actually works. 1. Basic Config (One-Time Setup) import logging, sys logging.basicConfig( level=logging.INFO, format="%(asctime)s | %(levelname)s |...
Building an LLM Client in Python
Don't let vendor SDKs leak into your business logic. Build one clean client. Swap providers later without touching your app. The Interface from abc import ABC, abstractmethod from dataclasses import dataclass @dataclass class LLMResponse: text: str tokens_used: int model: str...
Calling AI APIs with Python
Most AI APIs look simple in the docs. In production, you need retries, error handling, streaming, and batching. Here's the distilled playbook. The Modern Stack Library Best For openai OpenAI, Azure OpenAI anthropic Claude google-genai Gemini httpx Generic REST APIs tenacity...
Python Async Programming for AI
AI workloads are I/O monsters. You're waiting on OpenAI's API, streaming tokens from Claude, fetching embeddings from a vector DB, or pulling training data from S3. Standard synchronous Python processes these one by one. Async lets you orchestrate thousands of these waits...
Managing AI Dependencies & API Keys: A Developer's Survival Guide
AI projects collapse for two predictable reasons: dependencies that won't install, and API keys that leak on GitHub. Here's how to handle both without the drama. Lock Down Your Dependencies AI's dependency stack is a house of cards. PyTorch wants CUDA 11.8. Transformers needs...
Managing AI Dependencies with requirements.txt
Building AI applications today feels like assembling a spaceship from parts made by a thousand different manufacturers. You've got PyTorch or TensorFlow for deep learning, Hugging Face Transformers for NLP, OpenCV for computer vision, NumPy and Pandas for data wrangling, plus...
Conda Environments for AI Development
The previous post covered venv as the standard, built-in way to isolate Python dependencies. But anyone who's spent time in AI or data science circles has run into its limits fast — usually the moment a project needs a specific CUDA version, a non-Python library, or a complex...
Python Virtual Environments for GenAI
It's one of those things that feels like pure overhead the first time you skip it — and then, a few months and a few projects later, becomes the thing that saves you from a very specific, very avoidable kind of pain: two projects on the same machine quietly fighting over …
Python Exception Handling in AI Projects
API calls fail. Rate limits get hit, networks hiccup, models return malformed output, timeouts happen mid-generation. None of this is exotic — it's the normal, expected texture of building anything that talks to an external AI service. What separates a fragile AI script from...
Python JSON Handling for LLM Applications
Every structured piece of data that moves between your code and an LLM API eventually passes through JSON — it's the universal handshake format for AI APIs, tool calls, and structured model output. Python's relationship with JSON is close to seamless, but getting genuinely...
Python Lists and Dictionaries in AI
If there's one pattern that shows up in nearly every line of generative AI code, it's this: lists and dictionaries, nested inside each other, moving data in and out of API calls. They've been referenced throughout this series as "the core data shapes of AI work," but they deserve a …
Python OOP for GenAI Projects
The previous post covered classes as a practical tool for managing state in AI applications — conversations, sessions, agents. This one zooms out to the bigger picture: object-oriented programming (OOP) as a design philosophy, and specifically the four core principles —...
Python Classes for AI Applications
Functions are great for a single, self-contained piece of logic — summarize this text, classify this sentiment. But a lot of real generative AI applications need something functions alone don't handle well: state that persists and evolves over time — an ongoing conversation,...
Python Functions for AI Development
Once a generative AI script grows past a handful of lines, functions stop being optional structure and start being the thing that keeps the whole project sane. They're how you turn "a prompt I typed once" into "a reusable piece of AI logic anyone on the team can call." This …
Python Variables for AI Applications
Every generative AI script — no matter how simple or sophisticated — is built on the most basic unit in Python: the variable. It's easy to skim past variables as "too basic to matter" when you're eager to get to prompts and API calls, but the way you use variables directly shapes …
Python Fundamentals for GenAI Developers
You don't need to be a professional software engineer to start building with generative AI — but there's a core set of Python fundamentals that make working with LLMs, APIs, and AI frameworks dramatically smoother. If you're coming to Python specifically to build generative...
Prompt Versioning
Prompts start out as quick, one-off strings scattered through a codebase. Then a change to one "small tweak" quietly breaks a feature in production, nobody remembers what the prompt looked like last week, and there's no way to tell whether the new wording actually performed...