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Prompt Engineering Intermediate

Context Windows, Chunking and Compression: Feed the Model Right

Fit the right material into a context window and cut token spend without losing accuracy.

Rated 4.0 out of 5 from 2 reviews 24 students

PR Created by Priya Raman

  • Last updated August 2026
  • English
  • 4h of material
  • 30 lessons

What you will learn

7 concrete outcomes

Every bullet below is something you will have built, shipped or be able to explain by the time you finish the last lesson.

  • Count tokens accurately and see where budget is actually going in a request
  • Choose a chunking strategy that matches the structure of your source material
  • Use overlap and recursive splitting without duplicating half your corpus
  • Summarise conversation history while preserving the decisions that matter
  • Lay out long prompts with position effects in mind
  • Decide when compression saves money and when it quietly costs accuracy
  • Measure every change against a fixed evaluation set instead of intuition

Course curriculum

6 modules · 30 lessons · 4h of material

5 lessons running 36m in total. Each lesson ships with the finished source files and a short written recap, so you can follow along in your own editor and skim the module again later.

Lesson-by-lesson titles, code downloads and exercises live inside the course library you get access to straight after checkout.

5 lessons running 44m in total. Each lesson ships with the finished source files and a short written recap, so you can follow along in your own editor and skim the module again later.

Lesson-by-lesson titles, code downloads and exercises live inside the course library you get access to straight after checkout.

5 lessons running 40m in total. Each lesson ships with the finished source files and a short written recap, so you can follow along in your own editor and skim the module again later.

Lesson-by-lesson titles, code downloads and exercises live inside the course library you get access to straight after checkout.

5 lessons running 42m in total. Each lesson ships with the finished source files and a short written recap, so you can follow along in your own editor and skim the module again later.

Lesson-by-lesson titles, code downloads and exercises live inside the course library you get access to straight after checkout.

5 lessons running 38m in total. Each lesson ships with the finished source files and a short written recap, so you can follow along in your own editor and skim the module again later.

Lesson-by-lesson titles, code downloads and exercises live inside the course library you get access to straight after checkout.

5 lessons running 40m in total. Each lesson ships with the finished source files and a short written recap, so you can follow along in your own editor and skim the module again later.

Lesson-by-lesson titles, code downloads and exercises live inside the course library you get access to straight after checkout.

6 modules · 30 lessons

4h total length

Requirements

Short list, and deliberately so. If you meet these you can start today.

  • Comfortable writing Python
  • Some experience calling a model API and handling its responses
  • Basic familiarity with retrieval or search concepts is helpful but not required

About this course

A large context window is an invitation to be careless, and carelessness has a bill attached. This course treats the context window as the scarce, expensive resource it is, and teaches you to decide what earns a place in it.

You start with measurement: tokenisation in practice, counting before you send, and where the cost sits in a typical request once system prompt, retrieved passages, conversation history and expected output are all accounted for. Then you attack each part. Chunking strategies get proper treatment, fixed size against sentence and paragraph boundaries, structural splitting for code and Markdown, overlap and what it really buys you, and the recursive approaches that keep a heading attached to the text beneath it.

Compression comes next. Summarising conversation history without dropping the decision that mattered, hierarchical summaries for long documents, extracting only the fields a later stage needs, and knowing when compression costs more than it saves. You will also study position effects, the well-documented tendency for material in the middle of a long context to be used less reliably than material at either end, and how to lay out a prompt with that in mind.

Every technique is measured against a fixed evaluation set so you can see the accuracy and cost trade rather than guess at it.

Topics covered

Frequently asked questions

Still unsure about something? Write to misteryjj100@gmail.com and a human answers, usually the same working day.

It is the half of retrieval that decides whether the rest works. It pairs naturally with the RAG pipeline course, which covers the search side in depth.

Plain Python with a tokeniser library for the core work, plus LangChain splitters shown for comparison so you can judge whether the abstraction earns its place.

No. Larger windows raise the ceiling, not the accuracy. The course measures how retrieval quality and layout still dominate results at every window size.

Checkout is handled on our provider's secure payment page. The moment your payment clears we email your personal access link and access code to the address you used at checkout, and the same link appears in your account library. There is nothing to install and nothing to wait for.

Email misteryjj100@gmail.com within 14 days of your purchase, quote your order number, and we refund the full amount to your original payment method. No form to fill in and no questions about how much of the course you watched.

What students say

Reviews are written by people who bought this course. We publish the critical ones too.

4.0

Rated 4.0 out of 5

Course rating · 2 reviews

Rating distribution

  • 5 stars 0%
  • 4 stars 100%
  • 3 stars 0%
  • 2 stars 0%
  • 1 star 0%
  • LV

    Leon Vermeulen

    AI engineer

    Jan 2026
    Rated 4.0 out of 5

    Structural splitting earned its keep

    Splitting code along the syntax tree instead of at a character count improved our retrieval measurably. Held back from five by the position-effects material, which is presented as far more settled than I think it is when every figure quoted comes from one family of models.
  • SL

    Sofie Larsen

    Software engineer

    Nov 2025
    Rated 4.0 out of 5

    Repetitive through the middle

    The chunking comparison is thorough and the chapter on measuring what a change costs you is the sort of thing far too few courses bother with. The history compression section then covers three approaches that are near enough the same approach, and it starts to feel like filler.

Your instructor

PR

Priya Raman

Applied AI and machine-learning engineer

  • 236 students taught
  • 13 courses published
  • 4.3 instructor rating
  • Machine learning
  • RAG
  • Fine-tuning
  • LLM agents

Priya builds language-model features for a document-heavy SaaS product, which means she has taken retrieval and fine-tuning from a promising notebook to something on call at three in the morning. She teaches the mathematics only where it changes a decision you are about to make, and spends the rest of the time on data quality, evaluation and the cost of an agent that loops. Her courses run on a laptop and a modest API budget, so nobody has to rent a cluster to follow along. She publishes reproducible notebooks alongside every module.

See every course by Priya Raman
$24 USD

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