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Context Window Management

Learn Context Window Management for free with explanations, exercises, and a quick test (for NLP Engineer).

Published: January 5, 2026 | Updated: January 5, 2026

Table of Contents

Why this matters

Large Language Models are limited by a context window (the maximum tokens they can read at once). As an NLP Engineer, you must fit instructions, tools, chat history, and retrieved knowledge into that window without losing crucial information. Good context management boosts accuracy, cuts costs, and prevents truncation errors.

  • Real task: Build a RAG chatbot that answers from a 500-page manual without exceeding a 8k–32k token limit.
  • Real task: Summarize a 200-page policy while preserving definitions and exceptions.
  • Real task: Code assistant: pull the right files, compress them, and cite exact lines.

Concept explained simply

The context window is the model’s working memory. You must choose what to include now and what to leave out. Think like a luggage packer with weight limits: prioritize essentials, compress clothes, and label pockets.

Key terms:
  • Token: A chunk of text (word pieces, punctuation). Limits are per-token.
  • Context window: Maximum tokens the model sees at once (prompt + response).
  • Budgeting: Reserving tokens for system/instructions/tools/history/docs/answer.
  • Chunking: Cutting documents into retrieval-friendly pieces with overlap to keep coherence.
  • Compression: Summarizing, selecting, or rewriting content to fit.
Mental model in one sentence

Context = Instruction clarity + Minimal history + Only the most relevant knowledge, compressed to fit.

A practical 5-step context budget

1) Define the task and limits
  • Window size (e.g., 8k, 32k, 128k tokens).
  • What must be preserved: constraints, definitions, citations, code lines.
  • Latency and cost targets.
2) Budget tokens
  • System + tools: 10–25%
  • Task instructions: 5–15%
  • Chat history: 10–20% (summarize aggressively)
  • Retrieved docs: 30–60%
  • Answer allowance: 10–20%
3) Prepare knowledge
  • Chunk prose at ~300–800 tokens; overlap 10–20%.
  • For code: smaller chunks (80–200 tokens) around functions/classes; link to file summaries.
  • Build hierarchical views: global summary β†’ section summary β†’ snippet.
4) Assemble prompt with guardrails
  • Put instructions before knowledge. Ask for citations and uncertainty handling.
  • Include only top-k most relevant chunks; rerank by similarity + recency + metadata.
  • Compress docs to bullet points that directly answer the query.
5) Monitor and adapt
  • Track token usage per section and truncation events.
  • If near limit: reduce k β†’ compress β†’ summarize history β†’ shorten instructions.

Worked examples

Example 1 β€” Customer support RAG (8k window)

  • Budget: system/tools 1.2k, instructions 0.6k, history 0.6k, docs 4k, answer 1.6k.
  • Chunking: 500-token chunks, 15% overlap. Top-k = 3–5 after rerank.
  • Compression: Turn chunks into bullet evidence first; keep error codes and parameter values verbatim.
  • Fallback: If tokens > 4k for docs, compress to 2–3 bullets per chunk.
Why these numbers?

Support answers need space for citations and steps. 500-token chunks balance coherence and retrieval precision. Overlap preserves cross-sentence context.

Example 2 β€” Code assistant (32k window)

  • Budget: system/tools 3k, instructions 1k, history 2k, docs 18k, answer 8k.
  • Strategy: For a large repo, take a file-level summary (200–400 tokens/file) + exact function snippets (80–150 tokens each).
  • Retrieval: Embed functions and docstrings; rerank by filename match + symbol reference.
  • Compression: Preserve signatures and error messages exactly; summarize comments.
Edge case: huge files

Use hierarchical retrieval: project summary β†’ file summary β†’ function snippet. Don’t include entire files; target the symbols mentioned in the user query.

Example 3 β€” Policy Q&A (128k window)

  • Even with a large window, aim to keep docs ≀ 60% of budget for robustness.
  • Map-reduce: Summarize sections locally, then merge into targeted notes for the query.
  • Keep definitions and exceptions verbatim; compress examples.
  • Ask the model to list unresolved ambiguities explicitly.

How to choose chunking and overlap

  • Prose: 300–800 tokens, 10–20% overlap.
  • Tables/FAQs: keep row/QA pair intact; chunk by logical units.
  • Code: 80–200 tokens around functions/classes; zero or minimal overlap.
  • Papers/policies: 400–600 tokens aligned to headings.
Quick heuristics
  • If retrieval feels too broad β†’ smaller chunks.
  • If answers lack context β†’ slightly larger chunks or add overlap.
  • Always align chunks to semantic boundaries (headings, functions).

Compression strategies that work

  • Answer-targeted summaries: keep only lines that answer the current question.
  • Bulletized evidence: short bullets with citations (doc, section, line).
  • Selective quoting: exact quotes for definitions, error codes, API signatures.
  • Multi-query retrieval: generate variants of the user query to catch synonyms; merge and dedupe.
  • Reranking: combine semantic score with metadata (recency, section importance).
When you hit the limit
  1. Reduce top-k by 1–2.
  2. Compress chunks to bullets.
  3. Summarize older chat history.
  4. Trim verbose instructions while keeping constraints.

Quality and cost trade-offs

  • Large windows reduce orchestration complexity but can be slower.
  • Small windows force tighter retrieval and better compression, often improving precision.
  • Balance: reserve minimum tokens for a clear instruction and citations; optimize the rest dynamically.

Exercises

Complete the exercises below. A short checklist follows to sanity-check your work.

Exercise 1 β€” Design a token budget for a support chatbot (ID: ex1)

You have an 8k-token model. You must answer error troubleshooting questions using product docs. Include: system/tools, instructions, minimal history, retrieved docs, and an answer.

  1. Propose numeric budgets for each section.
  2. Choose chunk size, overlap, and top-k.
  3. Describe what to compress if you exceed the limit.
Expected output

A concise plan with numbers for each section, chunking, k, and a fallback compression sequence.

Exercise 2 β€” Chunking strategy across content types (ID: ex2)

Design chunking for three sources: (A) product manual with headings and bullet lists, (B) codebase with long files and functions, (C) policy PDF with long sentences.

  1. Propose chunk sizes and overlap per source.
  2. Define retrieval top-k and reranking signals.
  3. State what must be quoted verbatim vs summarized.
Expected output

Three mini-specs: sizes, overlap, k, rerank features, and compression rules for each source.

Checklist β€” Pre-flight before deployment

  • Instruction fits in < 15% and is specific.
  • Chat history summarized to last 1–3 turns unless critical context is needed.
  • Top-k chosen via validation, not guesswork.
  • Chunks aligned to headings/functions with appropriate overlap.
  • Compression keeps definitions, parameters, and citations verbatim.
  • No truncation when measuring with max-length queries.

Common mistakes and self-check

  • Mistake: Over-stuffing many chunks. Fix: Reduce k and compress to bullets.
  • Mistake: Long chat history. Fix: Summarize older turns; keep only active constraints.
  • Mistake: Chunking mid-sentence or mid-function. Fix: Align to boundaries.
  • Mistake: Missing citations. Fix: Reserve tokens for source markers; quote critical lines.
  • Mistake: Vague instructions. Fix: State task, constraints, format, and when to abstain.
Self-check prompts
  • Does any section regularly cause truncation?
  • Do answers remain accurate when I add 20% extra chat history?
  • Do citations point to the exact source lines?

Practical projects

  • Build a RAG FAQ bot: validate different chunk sizes (300, 500, 800) and overlaps (0%, 10%, 20%). Report accuracy and token usage.
  • Code helper: implement hierarchical retrieval (repo summary β†’ file summary β†’ function snippet) and measure latency vs accuracy.
  • Policy Q&A: implement query compression into answer-targeted bullets and compare with raw chunk stuffing.

Mini challenge

Your 8k model starts truncating citations. You cannot change the model. Propose a 3-step change list that fixes truncation while keeping answer quality. Keep it under 5 sentences.

Who this is for

  • NLP engineers building RAG apps, chatbots, and summarizers.
  • Data scientists deploying LLMs with retrieval or tool-calling.

Prerequisites

  • Basic prompt engineering (system/instruction roles).
  • Understanding of embeddings and vector search.
  • Familiarity with tokenization concepts.

Learning path

  1. Prompt structure and tokenization basics.
  2. Chunking and embedding strategies.
  3. RAG retrieval, reranking, and compression.
  4. Context budgeting and monitoring.
  5. Evaluation: accuracy, grounding, and latency.

Next steps

  • Instrument token usage and truncation rates.
  • A/B test chunk sizes and k on real queries.
  • Introduce answer-targeted compression and measure grounding.

Quick Test

Take the quick test below to check your understanding. Everyone can take it for free; only logged-in users get saved progress.

Practice Exercises

2 exercises to complete

Instructions

You have an 8k-token model to answer troubleshooting questions using product docs. Allocate tokens to: system/tools, instructions, chat history, retrieved docs, and the model’s answer.

  1. Propose numeric budgets for each section with a 10% safety margin.
  2. Choose chunk size, overlap, and top-k.
  3. List a 3-step fallback if you exceed the limit.
Expected Output
A concise plan with token numbers per section, chunking choice, top-k, and a clear fallback sequence.

Context Window Management β€” Quick Test

Test your knowledge with 8 questions. Pass with 70% or higher.

8 questions70% to pass
Prompt Design For ReliabilityReranking And Context Selection

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