Syllabus Live

Syllabus Module 1.2

Context Window

Large Language Models operate within rigid memory budgets. In this module, you will learn to manage context capacities, implement message-trimming strategies, summarize chat histories recursively, and build context-aware prompts that maximize information density while minimizing API costs.

IntermediateSystem Design FocusVRAM Economics

What You Will Master

Understand the mechanics of context windows and context budget allocation.

Implement sliding window conversation history truncation models.

Utilize summarization loops to compress historical conversational turns.

Understand RAG query packaging constraints and avoid needle-in-a-haystack decay.

Optimize prompt lengths to minimize latency, token consumption, and cost.

Handle context overflow scenarios and implement API error fallbacks.

The LLM Context Window Pipeline

Click on any step of the horizontal sequence to see how raw text resolves into vectors.

Active step: Text InputsContext Visualizer

The raw text block from chat, RAG retrievals, and system instructions.

State output:

"Retrieve relevant docs..."

Learning Path Lessons

6 submodules ready

Submodule 01

What is a Context Window?

Understand model memory capacities, input/output splits, and token bounds.

Beginner

Outcomes you will master:

•Explain memory limitations

•Describe context quadratic scaling

•Differentiate input/output budgets

Duration: 10 min readRead Lesson

Submodule 02

Context Budget Management

Learn session history scaling, system overheads, and token constraints.

Intermediate

Outcomes you will master:

•Track message inflation

•Allocate space for output tokens

•Set warning thresholds

Duration: 12 min readRead Lesson

Submodule 03

Prompt Trimming & Memory

Implement sliding windows, summarization memory, and truncation logics.

Intermediate

Outcomes you will master:

•Implement history sliders

•Compare sliding windows vs summarizers

•Build text truncation loops

Duration: 15 min readRead Lesson

Submodule 04

Sliding Window Conversation State

Manage dynamic conversation histories using sliding token limits.

Intermediate

Outcomes you will master:

•Track active chat queues

•Prune history based on tiktoken indices

•Keep system prompts pinned

Duration: 11 min readRead Lesson

Submodule 05

Context Overflow Failure Modes

Debug context window overflow errors and build automatic repair gates.

Advanced

Outcomes you will master:

•Diagnose 400 Bad Request errors

•Implement prompt compression

•Structure fallback models routing

Duration: 14 min readRead Lesson

Submodule 06

Context Engineering in Interviews

Prepare for engineering interviews focused on long-context architecture.

Interview

Outcomes you will master:

•Discuss 'lost-in-the-middle' retrieval

•Defend history compression

•Settle memory budgets

Duration: 15 min readRead Lesson

Module Status

0%completed

6 Lessonsto complete

Quick Cheatsheet

1.Total tokens = Input prompt + Output generation. Keep a safety buffer.
2.Self-attention compute scales quadratically O(N^2) with sequence length.
3.The 'Lost in the Middle' rule: LLMs recall facts at prompt extremes best.
4.Always pin the System Prompt; truncate only User/Assistant chat history.
5.Use local tokenizers (tiktoken) to calculate bounds before hitting the API.

Syllabus Project

Context Window Diagnostics

Build a diagnostic dashboard to simulate token budgeting, sliding window truncation, and history summaries.

View GitHub View Project Requirements →

Build What You Learn

Capstone Project: Context Window Diagnostics

Build a diagnostic dashboard to simulate token budgeting, sliding window truncation, and history summaries.

Stack Planned

Next.jsTypeScriptTailwind CSStiktoken

View GitHub View Project Requirements

Interview Readiness

Interview Defense Checklists

Open Interview Guide

Question 01How do you prevent context window exhaustion in multi-turn conversation agents?

Question 02Explain the 'Lost in the Middle' phenomenon in long context windows.

Question 03What are the trade-offs of using sliding windows vs summarization memory?

Question 04How do you calculate and reserve space for output tokens in a strict budget plan?

Question 05How do you evaluate if a model is successfully retrieving information from a 100k token window?

Open Interview Guide

Production Best Practices Checklist

1Calculate prompt token size using tiktoken before executing API requests

2Enforce hard caps on history message counts and prune oldest entries dynamically

3Utilize background summarization for conversations extending beyond 10 turns

4Inject RAG documents prioritized by semantic relevance scores

5Reserve at least 15% of the total context window for the model's generated answer

6Setup monitoring alerts for API errors returning context window length violations

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