Why LangChain is Used in GenAI
The Raw LLM Problem When developers first experiment with LLMs, the experience is deceptively simple. You install a client library, pass a string to an API, and receive an impressive response. But this simplicity masks enormous complexity when you attempt to build...
What is LangChain?
Introduction: The Orchestration Gap Large Language Models like GPT-4, Claude, and Gemini are remarkable at reasoning, writing, and coding. But when you try to build a real application with them, you quickly hit a wall. An LLM, by itself, is stateless. It cannot browse the...
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...
Asyncio in LLM Applications
LLM APIs are high-latency, I/O-bound black holes. A single GPT-4 call takes 1–10 seconds. Do that synchronously in a loop, and you're burning wall-clock time watching network requests finish one by one. Asyncio fixes this by letting Python juggle hundreds of in-flight...
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...
Building Reliable LLM Prompts
This series has covered dozens of individual techniques and concepts — structure, examples, chain-of-thought, sampling parameters, testing, versioning, context engineering. "Reliability" is really the thread tying all of it together: not just getting a good response once, but...
Prompt Testing Strategies
A prompt that looks great on the one example you tried it on can still fall apart the moment real, messier input hits it. The gap between "seems to work" and "actually reliable" is exactly where prompt testing lives. This post pulls together a practical set of strategies for testing …
Reusable Prompt Templates
Somewhere between "type a fresh prompt every time" and "build a full internal prompt library," most people and teams land on the same practical habit: taking a prompt that worked well once and turning it into something they can use again and again, with just the details...
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...
Context Engineering vs Prompt Engineering
As AI applications have grown more sophisticated — pulling in documents, memory, tool outputs, and conversation history rather than just a single well-crafted instruction — a new term has entered the vocabulary: context engineering. It's often used alongside, or even instead...
Preventing Prompt Injection
The last post in this series explained what prompt injection is and why it's structurally hard to fully solve. This one is the practical follow-up: if you're building an AI application — or just deploying one responsibly — what actually reduces the risk in practice? There's...
Prompt Injection Explained
Every technique covered in this series so far has been about getting an AI model to do what you want. Prompt injection flips that entirely — it's what happens when someone else's text, buried inside content the model is processing, hijacks the model into doing what they want...