AI Systems · advanced

Context Packing

Ordering and budgeting retrieved context within the model's window.

ai-systemsrag

Mental model

Context is a budget. Models attend unevenly (lost-in-the-middle), so order matters: put the strongest evidence at the edges, dedupe, and cut before you overflow.

How to study Context Packing

Begin by restating the mental model in your own words, then connect it to a concrete system you have built or operated. Name the mechanism, the constraint it addresses, and the trade-off it introduces. Use Lost in the Middle (Liu et al.) to check details, but close the source before writing your explanation. Retrieval is the learning step; rereading is only preparation.

Next, compare Context Packing with RAG, Reranking. Ask what changes in correctness, latency, resource use, operability, and failure recovery. Complete Pack chunks into context budget and preserve the command, input, output, and one failed attempt as evidence. Finish by explaining the idea without jargon to someone who has not studied the track.

Proof of understanding

  • Explain the mechanism from first principles and identify the state it reads or changes.
  • Give one situation where the concept is the right choice and one where it is not.
  • Predict a realistic failure mode before running the drill, then compare the prediction with evidence.
  • Connect the result to a roadmap or build artifact instead of treating the concept as isolated trivia.

Common mistakes

  • Filling the whole window because it is available
  • Ignoring position effects on attention

Learn from primary sources

Practice and explain it back

Pack chunks into context budget

Budget 800 tokens. Chunks A=300,B=400,C=250 relevance [0.9,0.85,0.7]. Greedy pack by relevance until full. Which included?

Expected evidence: A+B=700; C does not fit entirely — include partial or skip per policy.

Open the interactive drill →

Review prompts

  • What is the 'lost in the middle' effect and how do you mitigate it?

Build evidence

Use a roadmap capstone to turn this concept into working evidence.

Prerequisites

Related concepts

Learning paths

None assigned yet.