How-to guide17 min
Getting good results from AI coding assistants
Give context, work in small steps, verify with tests — a workflow that makes AI-written code something you can trust.
PracticalDevelopers
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Explainer9 min
RAG explained: handing AI a reference book
How do chatbots answer from your company's documents? Here's how RAG, a pattern widely used in AI products, works.
BeginnerBeginnersDevelopers
Explainer8 min
Tokens and pricing, explained simply
What "$2 per million tokens" on an AI price list means, and how to estimate what your own usage would cost.
BeginnerBeginnersOffice workersDevelopers
How-to guide14 min
Your first LLM API call: from key to response
Using the Claude API as the example: keep your API key safe, send a first request from Python, and read the answer, the stop reason and the token usage.
BeginnerDevelopers
How-to guide15 min
Get structured (JSON) output from an LLM
Instead of asking for JSON and hoping, define the shape as a schema and get responses your code can use directly. Worked through with a customer-message classifier.
PracticalDevelopers
How-to guide15 min
Cut API costs with prompt caching
Cache the long instructions and documents you resend with every request, and their input cost drops sharply. Using a support bot built on a manual, here's how to place the cache point and check that it works.
PracticalDevelopers
Explainer9 min
What is an AI agent?
Unlike a chatbot that only answers, an agent takes a goal and keeps going, using tools and checking results, until the job is done. How it works, what it suits, and what to settle before you hand it work.
BeginnerBeginnersDevelopers
How-to guide18 min
A minimal RAG chatbot with ten documents
Build a Python chatbot that searches ten internal policy documents with embeddings and answers only from what it finds, then test search and answers separately and fix what breaks.
PracticalDevelopers