Long-Context Prompts That Don't Get Lost

Intermediate

A million tokens in, one relevant needle out

01 The Concept

Modern models accept enormous inputs — whole codebases, hundred-page contracts, a year of meeting notes. But 'fits in context' ≠ 'gets used well'. Attention over long inputs is uneven: material in the middle of a giant prompt gets less weight than the start and end, and models skim just like tired humans.

Three habits fix most of it. Structure the haystack: label and delimit each document so sections are addressable ('in …'). Position deliberately: long material first, your question and instructions last, restated. Force evidence: require verbatim quotes with locations before conclusions — quote-then-answer converts skimming into lookup, and makes hallucinated 'findings' immediately visible.

02 Weak vs. Strong

EX 01Comparing two contracts

Two contracts follow, tagged (our current vendor) and (proposed replacement).

TASK PREVIEW (details after the documents): compare liability & indemnification.

TASK:

From each contract, quote verbatim every clause about liability caps, indemnification, and exclusions — with its section number.
Build a side-by-side table from those quotes only.
Flag anything present in one contract but absent in the other — absence is a finding.
End with: which sections of either document you did not read closely.

If a topic isn't covered in the quotes you found, say 'not found' — do not summarize from memory of typical contracts.

→ Why it works

Tagged documents, task restated after the material, quotes-with-locations before conclusions, absence treated as signal, and a coverage confession. Each habit closes a specific long-context failure.

EX 02Asking about a big codebase dump

40 source files follow, each preceded by === FILE: path ===.

[files]

TASK: hypothesize why checkout latency doubled. Rules:

Cite evidence as file:line for every claim ('checkout.go:112 acquires the lock inside the loop')
Rank your top 3 hypotheses by likelihood, each with its evidence and the experiment that would confirm it
List the files you treated as irrelevant, in one line
If the cause likely lives in a file NOT provided (config, infra, DB schema), say exactly what you'd need

→ Why it works

File-line citations make claims checkable; ranked falsifiable hypotheses beat one confident guess; and the 'what's missing' clause stops the model pinning the crime on whatever code happens to be visible.

03 Key Points

01Big context ≠ attentive context: middles get skimmed — structure and position accordingly

02Label every document: , — so both you and the model can address sections

03Instructions go AFTER the long material (and can be previewed before it); the end of the prompt is prime attention

04Quote-then-answer: 'first give verbatim quotes with section references, then conclusions based only on those quotes'

05Ask for a coverage note: 'state which sections you did not use' — reveals silent skimming

06If it's really a search task, retrieve-then-read (RAG) still beats dumping everything in

04 Model-Specific Notes

Claude is trained for long-document work with XML tags and quote-first workflows — 'extract relevant quotes into before answering' is a documented, measurable win. Put instructions after the documents.

05 For Your Role

Pasting a 30-page insurance policy? Ask: 'quote every clause about water damage with its section number, then explain in plain English using only those quotes.'

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