Learn AI Development — ABXK

What Is AI Development? Using AI vs. Building with AI

Wed, 14 Oct 2026 00:00:00 +0000

Ask three people what “AI development” means, and you may hear three different answers. A programmer thinks of an assistant that suggests code in the editor. A product manager thinks of a chatbot that answers customer questions. A researcher thinks of training a model on huge amounts of data. All three are right, but they describe different work, with different skills, tools and costs.

This is part 1 of Level 0 in our AI Development learning path. You need no programming knowledge. You will learn the three ways to do AI development and which roles work on each. You will also see how this path is built, so you can decide where to start.

How an AI App Works: Model, Prompt, Data, Interface

Wed, 14 Oct 2026 00:00:00 +0000

You ask an online shop’s support chat a question and get a helpful answer. It feels like one smart program is talking to you. In fact, seven parts work together, and only one of them is the AI model. The rest is ordinary software that decides what the model sees and what happens with its answer.

This is part 2 of Level 0 in our AI Development learning path. Part 1 described an online shop that builds a support assistant on top of a finished model. Now you look inside such an app: you learn its seven parts and follow one customer question through all of them. You do not need to know how to code.

APIs, Tokens and Context Windows Explained

Wed, 14 Oct 2026 00:00:00 +0000

Most AI apps do not run a language model themselves. They send text to a model at a provider and get text back. Three ideas shape this exchange: the API that carries each request, the tokens in which the model counts every piece of text, and the context window that limits how much text fits into one request. Together, they decide what your app can do, how quickly it answers and what it costs.

No-Code, Low-Code or Code: Three Ways to Build with AI

Wed, 14 Oct 2026 00:00:00 +0000

Say you want an assistant that answers your colleagues’ questions about travel expenses. You could put it together on a platform by clicking, without a single line of code. You could use the same kind of platform and add a few lines of code where it falls short. Or a developer could write it as ordinary software. All three can lead to a working assistant. They differ in how fast you start, how much you decide yourself and how hard it is to change course later.

What AI Coding Assistants Do (and Where They Fail)

Wed, 14 Oct 2026 00:00:00 +0000

An AI coding assistant can write a working function in seconds. It explains confusing error messages, suggests tests and renames a variable across a whole project. The same assistant can also call a function that does not exist, pick a library version that is years old or leave a door open for attackers. And it does all of this in code that looks clean and confident.

This is part 5 of Level 0 in our AI Development learning path. You will learn the three main forms of coding assistants, the tasks where they save real time, the mistakes they typically make and why the review always stays with you. You do not need to program: a short code example shows the typical mistakes, and the text explains them in plain words.

Your First LLM API Call, Step by Step

Wed, 14 Oct 2026 00:00:00 +0000

Chat apps are the easiest way to use a large language model. But maybe you want AI inside your own software: a support form, a report generator or a small internal tool. Then your program has to talk to the model directly, through its API (application programming interface). Your code sends a request, and the answer comes back as data that your program can use.

This is part 1 of Level 1 in our AI Development learning path. You will create an API key, store it safely, send a first request in Python, read the response and handle errors. If tokens and context windows are new to you, start with APIs, Tokens and Context Windows Explained from Level 0.

Prompt Design for Developers: System Prompts and Structure

Wed, 14 Oct 2026 00:00:00 +0000

When you chat with an AI assistant, a vague prompt costs you one extra question. Inside an app, the same prompt runs hundreds or thousands of times, with input you never see in advance. A small weakness then turns into a steady stream of wrong answers. Prompt design for developers is about making prompts reliable, testable and safe to change.

This is part 2 of Level 1 in our AI Development learning path, and it builds on your first LLM API call. You will learn how to split instructions across message roles, structure a system prompt, separate instructions from data, use examples and JSON output, choose the temperature and keep versions of your prompts.

Build a Simple AI App Without Code

Wed, 14 Oct 2026 00:00:00 +0000

You do not need to write code to build a useful AI app. On a no-code platform, you put the app together in a visual editor: you write instructions, upload your own documents and choose a model from a list. The platform runs the app and gives you a chat window to share. In an afternoon, you can have an assistant that answers questions from your own files.

This is part 3 of Level 1 in our AI Development learning path. Parts 1 and 2 showed how to call a model from code and how to write prompts that work inside an app. Now you build an app without code, from start to finish: an FAQ assistant for a small coworking space. If you are not sure whether no-code fits your idea, read No-Code, Low-Code or Code from Level 0 first.

Choose a Model for Your App: Quality, Speed, Cost, Openness

Wed, 14 Oct 2026 00:00:00 +0000

In your first API call, the request named a placeholder model. In a real app, this one field is among your most important decisions. It sets the quality of the answers, how long users wait and what each request costs. There are many candidates: our LLM database alone lists 343 models.

This is part 4 of Level 1 in our AI Development learning path. You will learn which criteria matter and how they pull against each other. You will also see what open and closed weights mean for your app and how to test a shortlist on your own data. It helps to know the basics of tokens and context windows from Level 0.

Deploy a Small AI Web App

Wed, 14 Oct 2026 00:00:00 +0000

Your script from part 1 works on your laptop. To let other people use it, it has to run on the internet — without exposing your API key, without inviting abuse and without a surprise bill. Putting software on a server so that people can reach it is called deployment.

This is part 5, the last part of Level 1 in our AI Development learning path. You will set up the simplest sound architecture for a small AI web app: a static page and one serverless function. You will learn where the API key lives, how to limit requests and costs, how to use your own domain with HTTPS and what to watch after launch. The guide builds on your first LLM API call and on the parts of an AI app from Level 0.

Embeddings and Vector Databases Explained

Wed, 14 Oct 2026 00:00:00 +0000

A customer types “Can I get my money back?” into your help center. The answer sits in an article called “Refund policy”, but the keyword search finds nothing, because the two texts share no words. Embeddings solve this problem. They turn text into numbers that capture its meaning, so software can find texts that mean the same thing even when the words differ.

This is part 1 of Level 2 (Intermediate) in our AI Development learning path. You will learn what an embedding is, how similarity search works, what a vector database adds and which settings matter in practice. Part 2 uses all of this to build a RAG pipeline step by step.

Retrieval-Augmented Generation (RAG), Step by Step

Wed, 14 Oct 2026 00:00:00 +0000

A language model only knows what was in its training data. It does not know your price list, your internal wiki or the policy change from last week. Retrieval-augmented generation (RAG) closes this gap. Before the model answers, your app looks up the most relevant passages in your own documents and adds them to the prompt. The model then answers from these passages and names its sources.

This is part 2 of Level 2. It builds on embeddings and vector databases from part 1. You will walk through the full pipeline step by step, learn where it typically breaks and how to test it — and when you do not need RAG at all.

Structured Output and Function Calling

Wed, 14 Oct 2026 00:00:00 +0000

A chat answer is written for people. Your code needs something else: fields it can read, values it can check and actions it can run. Structured output and function calling are the two techniques that turn a language model from a text generator into a reliable part of your software.

This is part 3 of Level 2. You will learn how to get valid JSON with a schema, how to validate and retry, how the function-calling loop works, how to write tool descriptions a model understands and which safety checks belong around every call. It builds on prompt design for developers from Level 1.

Test AI Features with Evals

Wed, 14 Oct 2026 00:00:00 +0000

Your ticket classifier from part 3 returns valid JSON every time. But does it choose the right category for every kind of ticket? Evals, short for evaluations, answer this question. An eval runs your AI feature on a fixed set of test cases and measures how often the result is good enough.

This is part 4 of Level 2 in our AI Development learning path. You will learn how evals differ from unit tests, how to build an eval set, which metric fits which answer and how to run evals automatically. It turns the small test set from prompt design for developers into a real quality check.

An AI-Assisted Coding Workflow That Stays Safe

Wed, 14 Oct 2026 00:00:00 +0000

An AI coding assistant can write a feature in minutes. Speed is not the problem. The problem is code that nobody really checked: an API key in a config file, a package that does not exist, a test the agent quietly changed so that it passes. A good workflow keeps the speed and adds gates: checkpoints that every change must pass before it moves on.

This is part 5 of Level 2 in our AI Development learning path. It turns the habits from what AI coding assistants do into a repeatable workflow: plan, generate, review, test, commit. You will learn how to brief an assistant, why small diffs and tests first matter, which automated checks catch secrets and risky packages, and how to review AI-written code with a checklist.

Build AI Agents: Tools, Loops and Memory

Wed, 14 Oct 2026 00:00:00 +0000

A chatbot answers a question. An AI agent works toward a goal. It decides the next step, uses tools such as a search or a database and reads the result. Then it continues until the job is done — or until a rule stops it. That small difference changes how you build, test and control the system.

This is part 1 of Level 3 (Advanced) in our AI Development path. You will build the agent loop step by step, choose tools and memory, set stop rules and approvals, and see how far today’s models get on long tasks. It builds on structured output and function calling from Level 2.

Web Data for AI Apps: Scraping, APIs and the Rules

Wed, 14 Oct 2026 00:00:00 +0000

Many AI apps need fresh data from the web: product prices, job ads, public tenders, documentation, news. A model alone cannot deliver it, because its knowledge stops at its training date. So your app has to collect the data — and how you collect it decides how reliable, legal and maintainable the result is.

This is part 2 of Level 3 (Advanced). You will learn the order of options (official API, licensed data, scraping), how a scraper works and which rules apply: robots.txt, terms of service, copyright and the GDPR. You will also see how to crawl politely, when proxies are legitimate and how to notice when a source changes. This is practical guidance, not legal advice; for real projects, involve your legal team.

GPU Infrastructure for AI: When to Rent Compute

Wed, 14 Oct 2026 00:00:00 +0000

Most AI apps never touch a GPU. They call a model through an API, and the provider runs the hardware. That changes when you fine-tune a model, run an open-weight model yourself or process millions of documents in one batch. Then you rent compute — and the bill depends on decisions you make before the first job starts.

This is part 3 of Level 3 (Advanced). You will learn when you really need GPUs, how to estimate GPU memory from model size, which way of renting fits which workload, and how to keep utilization high and costs under control. We explain the cost logic, not prices.

Fine-Tuning vs. RAG vs. Prompting: A Decision Guide

Wed, 14 Oct 2026 00:00:00 +0000

Your AI feature gives wrong answers, ignores your format or sounds nothing like your company. You have three ways to fix that. You can write better instructions (prompting). You can give the model the right documents at question time (retrieval-augmented generation, RAG). Or you can train the model further on your own examples (fine-tuning). Fine-tuning often sounds like the thorough solution. In practice, it takes the most effort to set up and to change — and it often fixes the wrong problem.

Observability for LLM Apps: Logs, Traces, Costs, Quality

Wed, 14 Oct 2026 00:00:00 +0000

Your LLM feature is live. Users ask questions, the model answers, and the bill grows. But does the feature work? Classic monitoring tells you that the server answered with status 200 in under a second. It does not tell you that the answer was wrong, that a prompt change doubled the tokens per request or that a search step found nothing for a whole week. Observability means that you can answer such questions from the data your app records — without guessing.

Production LLM Architecture: Gateways, Caching, Fallbacks

Wed, 14 Oct 2026 00:00:00 +0000

A prototype calls one model with one API key, and it works. A production system serves thousands of users, depends on providers that sometimes slow down or fail, and has to stay within a budget. What makes the difference is not a better prompt but the architecture around the model: one controlled entry point, caches, fallbacks, guardrails and targets you can measure.

This is part 1 of Level 4 (Expert) in our AI Development path. You will see a reference architecture, learn what each component does and what it costs, and finish with a checklist for the go-live. It builds on observability for LLM apps from Level 3: you can only run what you can see.

Self-Hosting Open-Weight Models

Wed, 14 Oct 2026 00:00:00 +0000

Through an API, you rent a model’s answers. When you self-host, you run the model on hardware you control. Open-weight models make this possible: their trained parameters — the weights — are published, so anyone can download and run them. With the weights, you also take over every job the API provider did for you: GPUs, scaling, updates, security and safety filters.

This is part 2 of Level 4 (Expert) in our AI Development path. You will learn when self-hosting pays off, how to read open-weight licenses, which software turns weights into an endpoint, how to size GPU memory, and how far open models trail closed ones in our data. It continues GPU infrastructure for AI from Level 3.

Multi-Agent Systems: Patterns and Pitfalls

Wed, 14 Oct 2026 00:00:00 +0000

One agent with good tools can do a lot. But some tasks are too broad for one context window, or they need different skills and permissions. Then teams split the work: one agent plans, several research in parallel, another one checks the result. Done well, this raises quality. Done badly, it multiplies cost, errors and confusion.

This is part 3 of Level 4 (Expert) in our AI Development path. You will learn four proven patterns, when several agents help and when they hurt, how errors and costs compound, how to trace a multi-agent run and where people must check the work. It builds on AI agents with tools, loops and memory from Level 3.

Coding Benchmarks Explained: SWE-bench, Aider, Terminal-Bench

Wed, 14 Oct 2026 00:00:00 +0000

A new model comes out, and the launch post shows a coding score a few points above the competition. Should your team switch? The number alone cannot tell you. You need to know which tasks stand behind it, how the result was produced and how much it can vary by chance.

This is part 4 of Level 4 (Expert) in our AI Development path. You will learn what SWE-bench Verified, Aider Polyglot and Terminal-Bench measure, how their tasks are scored, where the numbers mislead and how to build a small benchmark from your own repository. Level 0 introduced the first of these benchmarks in what AI coding assistants do; the methods here build on testing AI features with evals from Level 2.

Optimize LLM Cost and Latency at Scale

Wed, 14 Oct 2026 00:00:00 +0000

In a prototype, nobody watches the bill or the clock. At scale, both decide whether a feature survives: a small cost per request becomes a large monthly line, and a slow answer makes every user wait. Most LLM apps waste tokens and time in the same few places, so the same few levers help almost everywhere.

This is part 5 of Level 4 (Expert), the last part of our AI Development path. You will learn where cost and latency come from, how to measure the cost per successful task, which levers cut tokens and waiting time, and how a routing cascade sends easy requests to small models. Our article on how we reduce AI development costs covers the build phase; this part covers the running cost of every request.