Tutorials Prompt Engineering Tutorial
Tokens & Context Windows — Complete Guide
Tokens & Context Windows — Complete Guide: free step-by-step lesson with examples, common mistakes, and interview tips — part of Prompt Engineering Tutorial on Toolliyo Academy.
On this page
Prompt Engineering Tutorial · Lesson 3 of 100
Tokens & Context Windows
Prompts → Apps
Prompts · 1 — Basics · ~6 min · Module 1: Prompt Engineering Foundations
What is this?
Text is split into tokens — subword pieces billed and counted by the API. The context window is the max tokens the model can read in one request.
Why should you care?
PromptVerse RAG must fit retrieved chunks + system prompt + user message inside the window or answers get cut off.
See it live — copy this example
Copy the prompt into ChatGPT, Claude, or your LLM API playground and compare outputs.
const estimate = (text) => Math.ceil(text.length / 4);
const system = 120;
const user = 800;
const chunks = 6000;
const budget = 8192 - system - user - chunks;
// if budget < 0, trim or summarize chunksWhat happened?
- Rough char/4 estimates token use.
- You subtract fixed parts from the window limit.
- Negative budget means you must compress or retrieve fewer chunks.
Practice next
- Paste a 500-word doc into a tokenizer tool (or estimate).
- Plan a 8k window: system 200, user 300, rest for RAG.
- Decide how many chunks fit.
- Halve chunk count and compare answer quality.
- Swap a long system prompt for a 3-bullet version.
Remember
Tokens drive cost and limits. Budget system + user + retrieval + reply. Trim before you truncate mid-answer.
Context overflow
Enterprise search returns 20 large chunks.
Outcome: Pipeline ranks top 5 and summarizes the rest to stay under 128k.
Interview prep for this lesson
Practice these questions aloud after reading—each links to a full structured answer.
Sign in to ask a question or upvote helpful answers.
No questions yet — be the first to ask!
