Context Structure
The same information arranged differently can produce dramatically different results. Context Structure is the discipline of organizing, ordering, and delimiting the information you feed to a language model — turning a raw pile of facts into a coherent briefing the model can actually use.
Introduced: Research into how context organization affects LLM performance gained momentum in 2023, driven by work from multiple research groups including Liu et al.’s influential “Lost in the Middle” paper. As context windows expanded from 4K to 100K+ tokens, it became clear that simply dumping more information into a prompt was not enough — the structure and ordering of that information mattered as much as its content. Researchers demonstrated that models disproportionately attend to information at the beginning and end of their context, often neglecting material buried in the middle.
Modern LLM Status: While newer models like Claude, GPT-4, and Gemini have improved their ability to attend to information across long contexts, the fundamental principles of Context Structure remain highly relevant in practice. Well-organized prompts with clear delimiters, logical ordering, and explicit section headers consistently outperform unstructured information dumps — especially as prompts grow longer or incorporate multiple sources. Context Structure is not a single paper’s technique but a convergent set of best practices drawn from retrieval-augmented generation (RAG), prompt engineering research, and production LLM system design.
Organization Is a Force Multiplier
Language models process context sequentially, building an internal representation token by token. When information arrives as an unstructured wall of text, the model must do double duty: first figuring out what each piece of information is, then figuring out how it relates to the question at hand. Structure eliminates that first step by making the organization explicit.
Context Structure treats your prompt like a well-organized briefing document. Instead of a stream-of-consciousness dump, you provide clear sections, labeled boundaries, and a logical flow that mirrors how you want the model to reason. Delimiters like headers, XML tags, or markdown separators tell the model where one piece of context ends and another begins — preventing cross-contamination between unrelated information sources.
Think of it like the difference between handing someone a stack of unsorted papers and handing them a tabbed binder with a table of contents. The information is identical, but the structured version lets the reader find and connect what they need far more efficiently.
Research has shown that LLMs pay the most attention to information at the beginning and end of their context window, with recall dropping significantly for material in the middle. This U-shaped attention curve means that burying critical information in the center of a long prompt can cause the model to overlook it entirely. Context Structure techniques — like placing key facts at the start, using explicit section headers, and summarizing critical points — directly combat this phenomenon.
The Context Structure Process
Four steps from raw information to optimized prompt context
Audit Your Information Sources
Before writing the prompt, inventory all the information the model will need. Identify distinct sources — documents, data tables, conversation history, instructions, examples — and note where they overlap or conflict. This audit prevents you from accidentally burying critical context inside irrelevant material.
For a contract analysis task, you identify three sources: the contract text itself, the client’s specific questions, and relevant legal precedent summaries. Each serves a different role and should be clearly separated.
Establish a Logical Ordering
Arrange your context with the most important information first. Place your task instructions and key constraints at the beginning of the prompt, followed by the primary source material, then any supplementary context. If the model needs to reference specific facts to answer a question, position those facts before the question — not after it.
Structure: (1) Role and task description, (2) Primary document, (3) Supporting context, (4) Specific questions to answer. The model encounters the instructions first, then the evidence, then knows what to do with it.
Apply Delimiters and Section Headers
Use explicit markers to separate different types of content. XML tags, markdown headers, triple dashes, or labeled sections all work — the key is consistency. Delimiters prevent the model from confusing instructions with examples, or treating one document’s claims as another’s. They create a visual and semantic boundary the model can parse reliably.
Wrap each source in XML tags: <contract>...</contract>, <client-questions>...</client-questions>, <precedent>...</precedent>. The model can now reference each source by name without ambiguity.
Trim and Prioritize Within the Window
Context windows are large but not infinite. Remove irrelevant or redundant information, summarize lengthy passages where full text is not needed, and duplicate critical constraints at both the beginning and end of the prompt if the context is very long. This combats attention decay and ensures the model has headroom for its response.
A 50-page report is too long to include verbatim. Instead, include the executive summary in full, relevant sections quoted with page references, and a final instruction reminding the model to focus on Sections 3 and 7.
See the Difference
How structure transforms the same information into a better prompt
Unstructured Context
Here is an email from a customer who is upset about their order and also some notes from the warehouse team about inventory issues and the return policy says items must be returned within 30 days and the customer ordered on March 1st and the delivery was late by 2 weeks and the product was damaged when it arrived. The warehouse says they ran out of the original item. What should we tell the customer?
The model produces a generic apology that misses key details, conflates the inventory issue with the damage issue, and forgets to address the return policy timeline.
Structured Context
Task: Draft a customer response addressing all issues below.
Customer complaint: Order placed March 1st. Delivery delayed 2 weeks. Product arrived damaged.
Warehouse notes: Original item out of stock. Replacement available in 5 business days.
Return policy: 30-day return window from delivery date. Damaged items eligible for immediate replacement.
Tone: Empathetic, solution-focused.
The model addresses each issue separately: apologizes for the delay, acknowledges the damage, offers immediate replacement under the damage policy, confirms the customer is within the return window, and provides a clear timeline for the replacement shipment.
Practice Responsible AI
Always verify AI-generated content before use. AI systems can produce confident but incorrect responses. When using AI professionally, transparent disclosure is both best practice and increasingly a legal requirement.
48 US states now require AI transparency in key areas. Critical thinking remains your strongest tool against misinformation.
Context Structure in Action
See how deliberate organization improves real-world prompt outcomes
Role: You are a financial analyst reviewing a potential acquisition.
<target-company-financials>
Revenue: $12M (2024), $15M (2025 projected)
EBITDA margin: 18%
Customer concentration: Top client = 35% of revenue
</target-company-financials>
<market-research>
Industry growth rate: 12% CAGR
Key competitors: 3 major players, target holds 8% market share
Regulatory risk: Medium (pending legislation in Q3)
</market-research>
<deal-parameters>
Asking price: $45M (3x revenue)
Our budget ceiling: $40M
Integration timeline: 18 months
</deal-parameters>
Question: Provide a risk assessment with a go/no-go recommendation.
Each information source is wrapped in descriptive tags. The model can reference “target-company-financials” and “market-research” without confusing which numbers come from which source. The deal parameters are separated from the analysis inputs, and the question comes last so the model has all evidence in hand before it begins reasoning. This structure virtually eliminates the risk of the model attributing market growth numbers to the target company or vice versa.
Task: Synthesize these three sources into a briefing on the current state of enterprise cybersecurity policy.
<source-1 type=”survey” date=”2024-Q4”>
73% of enterprises have adopted zero-trust architecture. Phishing remains the top attack vector at 36% of breaches. Average time to detect a breach dropped from 207 to 194 days year-over-year.
</source-1>
<source-2 type=”academic” date=”2024”>
MIT study: Organizations with mandatory security training saw 45% fewer successful phishing attacks. Multi-factor authentication reduced account compromise by 99.2%. AI-powered threat detection produced 28% more false positives than rule-based systems.
</source-2>
<source-3 type=”industry-report” date=”2025-Q1”>
Ransomware payments exceeded $1.1 billion globally. Supply-chain attacks increased 78% over two years. Organizations using AI-driven security tools reported 40% faster incident response times.
</source-3>
Instructions: Attribute each claim to its source. Flag any contradictions between sources. Conclude with an evidence-based recommendation.
Numbered, typed, and dated source tags let the model attribute claims accurately. The instruction to “flag contradictions” leverages the clear separation — the model can compare source-1’s productivity claim against source-2’s findings without confusion. Without this structure, the model might blend survey data with academic findings and present them as a single consensus, obscuring important nuances.
<system-instructions priority=”highest”>
You are a medical triage assistant. Never provide diagnoses. Always recommend consulting a healthcare professional. Flag any mention of emergency symptoms.
</system-instructions>
<patient-context priority=”high”>
Age: 45, Male
Current medications: Lisinopril 10mg, Metformin 500mg
Known conditions: Type 2 diabetes, hypertension
</patient-context>
<symptom-report priority=”high”>
Patient reports persistent headaches for 3 days, mild dizziness when standing, and occasional blurred vision.
</symptom-report>
<reference-guidelines priority=”medium”>
Standard triage protocol: headache + dizziness + vision changes in hypertensive patient = urgent follow-up recommended within 24 hours.
</reference-guidelines>
Question: Based on the above, what triage recommendation should be made?
Priority tags create a clear hierarchy: safety instructions override everything, patient context and symptoms carry high weight, and reference guidelines inform but do not dictate. The model knows that its “never provide diagnoses” constraint is non-negotiable, while the triage guidelines are reference material. This hierarchical structure is essential in safety-critical applications where some context must always take precedence over others.
When to Use Context Structure
Best for complex, multi-source, or high-stakes prompts
Perfect For
When your prompt includes information from multiple sources that must be kept distinct — contracts, reports, emails, or data tables that the model needs to reference without cross-contamination.
When your prompt approaches or exceeds several thousand tokens, structure combats the “lost in the middle” effect and ensures the model attends to all critical information evenly.
When feeding retrieved documents into a prompt programmatically, consistent delimiters and metadata tags let the model distinguish between retrieved chunks and treat each with appropriate attribution.
When certain instructions must take absolute precedence — safety guardrails, compliance rules, or output constraints — hierarchical structure makes the priority ordering explicit.
Skip It When
For short, straightforward prompts with one piece of context and one question, elaborate structure adds overhead without benefit. Natural language works perfectly.
In back-and-forth chat, the conversation itself provides natural structure. Adding XML tags to every message creates friction without improving comprehension.
When you want the model to free-associate or explore ideas loosely, rigid structure can constrain the creative process rather than enhance it.
Use Cases
Where Context Structure delivers the most value
Legal Document Review
Separate contract clauses, regulatory requirements, and client instructions into labeled sections so the model can cross-reference without confusing which obligations belong to which party.
Research Synthesis
Tag each study or source with metadata like date, methodology, and sample size, enabling the model to weight evidence appropriately and flag conflicting findings.
RAG Pipeline Design
Structure retrieved document chunks with consistent delimiters and source attribution so the model can cite its sources and distinguish between retrieved facts and its own knowledge.
Customer Support Systems
Organize customer history, product documentation, and policy rules into distinct sections so the support model never confuses one customer’s case with another’s policies.
Compliance and Audit
Place non-negotiable safety constraints and regulatory requirements in high-priority sections that the model treats as absolute rules, separate from informational context.
Data Analysis Reports
Separate raw data, analysis instructions, and output format specifications so the model processes each independently and produces structured, reproducible results.
Where Context Structure Fits
Context Structure bridges basic prompting and systematic information architecture
Context Structure is not a standalone technique — it is the foundation that makes every other prompting method more effective. Chain-of-Thought works better when the input facts are clearly organized. Few-Shot Learning is more reliable when examples are delimited from instructions. RAG systems depend entirely on consistent context structure to present retrieved documents without confusion. Mastering context organization pays dividends across every prompting technique you use.
Related Techniques
Explore techniques that build on structured context
Structure Your Next Prompt
Apply Context Structure principles to your own prompts or use our tools to build well-organized, high-performance prompts from scratch.