Order the stages of a retrieval and assembly pipeline that exploits the lost-in-the-middle curve

Attempt it

1Send the assembled prompt to the model
2Keep the top 5 reranked chunks and discard the rest
3Restate the user question immediately above the retrieved block
4Interleave the top chunks into a head and tail layout with the strongest at top and second strongest at bottom
5Score the 50 candidates with a cross-encoder reranker
6Embed the user query and run vector search to fetch the top 50 candidate chunks

Quick reference

TL;DR

Retrieve wide, rerank, trim to top-5, then position the survivors to exploit the lost-in-the-middle curve and restate the question right before the block.

Memory aid

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Easy to grasp

Picture packing a tiny suitcase for a long trip. First you pull every plausible item out of the closet, then you sort them by how badly you actually need each one, then you keep only the few that fit. You do not put your passport at the bottom under the shoes. You put it on top where you can grab it, with a backup copy near the zipper for the return flight. Right before you close the case you check the boarding pass one more time so the next thing you do is aligned with the trip. A retrieval pipeline runs the same way. Pull wide, sort by quality, keep the best, place them where the model will actually read them, and remind it what the trip is about.

Key concepts

Context engineering Retrieval-augmented generation Vector databases Embeddings

Detailed answer & concept explanation

~5 min read

Everything you need to truly understand this topic: intuition, mechanics, step by step explanation, code, formulas, and worked example. Click to expand.

Everything you need to truly understand this topic: intuition, mechanics, step by step explanation, code, formulas, and worked example.

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Quick scan · 2–4 min

Everything important, quickly.

5 minutes: six stages and their dependencies, why each one cannot move, how positioning exploits the attention curve, where the question restate sits, common reorderings that break the pipeline.

Real-world examples

Real products, models, and research that use this idea.

Anthropic's Claude Opus 4.7 RAG cookbook recommends wide dense retrieval, cross-encoder rerank, top-5 trim, and explicit restate of the user query above the block.
Cohere's 2026 production RAG guide pairs Embed v4 with Rerank 3.5 and ships a positioning helper that interleaves chunks for head and tail layout.
LlamaIndex's PostprocessorPipeline exposes a SentenceReorderPostprocessor that explicitly implements lost-in-the-middle aware positioning after rerank.
LangChain's ContextualCompressionRetriever wraps rerank and trim into a single stage, leaving positioning to the downstream prompt builder.
Voyage AI's 2026 retrieval guide for technical corpora bundles rerank-2 with an end of prompt question restate as a default.

Interviewer follow-ups

What an interviewer would ask next. Try answering before peeking at the approach.

QWhere would you place a system prompt with hard rules relative to the retrieved block?

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QHow does this ordering change for a multi-turn agent that uses tool calls to retrieve lazily?

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QWhat goes wrong if you put the question both at the top and bottom of the retrieved block?

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Don't say this

Red flags and common mistakes that signal junior thinking. Click to expand.

Most common mistake

Reranking before retrieval is impossible; trimming before reranking wastes the precision step; positioning before trimming positions garbage. Stage order is not stylistic, it is causal.

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60-second cheat sheet

The night-before-the-interview bullets. Scan these on the way to the call.

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References

Primary sources. Skim if you want the original framing.