Structuring Prompts for 2M Token Contexts: Maintaining Retrieval Accuracy at Scale

The expansion of Large Language Model (LLM) context windows to 2 million tokens changes how we think about in-context learning. However, a larger context window does not guarantee perfect recall. Standard Needle In A Haystack (NIAH) tests often use simple, isolated keys. In real-world engineering scenarios, where you feed an entire codebase, database schema, and UX specification into a model, retrieval accuracy degrades significantly. This degradation is not uniform; it typically concentrates in the middle of the context window, a phenomenon known as the "lost in the middle" effect.

The Problem with Unstructured Context

When dealing with large context window prompting, developers often treat the context window as a database. This is a conceptual error. A database uses deterministic indexing to retrieve records. An LLM uses soft attention mechanisms that distribute weights across the entire input sequence. When the input sequence spans millions of tokens, the attention signal-to-noise ratio drops.

If you dump unstructured text, raw markdown files, and loose JSON schemas into a 2-million-token prompt, the model will struggle to resolve cross-references. For example, if a database schema is defined at token 200,000, and an API route handler is defined at token 1,500,000, the model may fail to connect the two when generating a new controller. To maintain high retrieval accuracy and prevent hallucination, we must apply strict structural patterns to our inputs.

The Anatomy of a Structured 2M Token Prompt

To optimize attention allocation, we must structure the prompt deterministically. We recommend a hierarchical XML-based structure. XML tags provide clear boundaries that the model's attention heads can easily parse.

Here is the recommended structural layout for a massive context prompt:

1. System Instructions and Constraints (Top)
2. Global Metadata and Dependency Graph
3. Static Reference Data (Schemas, API contracts)
4. Dynamic Codebase/Document Context (The bulk of the tokens)
5. Task-Specific Instructions and Query (Bottom)

Placing the query and the system instructions at the absolute boundaries (top and bottom) takes advantage of primacy and recency biases in transformer models. The middle of the context should be reserved for the dense, static reference material.

Implementing Context Zoning

Let us look at how to structure the dynamic codebase context. Instead of concatenating files raw, each file should be wrapped in an XML block containing metadata. This metadata acts as an index for the attention mechanism.

<context_zone id="codebase">
  <file path="src/models/user.ts" language="typescript">
    <dependencies>
      <dependency>src/types/auth.ts</dependency>
    </dependencies>
    <code>
      // File content goes here
    </code>
  </file>
</context_zone>

By explicitly declaring dependencies within the metadata tags, we assist the model in tracing execution paths without requiring it to infer relationships solely from the code structure.

Programmatic Prompt Assembly

Assembling a 2-million-token prompt manually is impractical. It must be done programmatically. Below is a Python pseudo-code example demonstrating how to build a structured context prompt from a directory, calculating token usage and injecting structural anchors.

# Pseudo-code for structured context assembly
import os
from typing import List, Dict

class ContextAssembler:
    def __init__(self, root_dir: str, max_tokens: int = 2000000):
        self.root_dir = root_dir
        self.max_tokens = max_tokens
        self.token_estimator_factor = 4  # Rough character-to-token ratio

    def estimate_tokens(self, text: str) -> int:
        return len(text) // self.token_estimator_factor

    def build_file_node(self, file_path: str) -> str:
        relative_path = os.path.relpath(file_path, self.root_dir)
        with open(file_path, 'r', encoding='utf-8') as f:
            content = f.read()

        return (
            f'<file path="{relative_path}">\n'
            f'<code>\n{content}\n</code>\n'
            f'</file>\n'
        )

    def assemble(self, query: str, system_instructions: str) -> str:
        prompt_parts = []

        # 1. System Instructions at the top
        prompt_parts.append("<system_instructions>\n" + system_instructions + "\n</system_instructions>")

        # 2. Open Context Zone
        prompt_parts.append("<context_zone id=\"source_code\">")

        current_tokens = self.estimate_tokens("".join(prompt_parts))

        for root, _, files in os.walk(self.root_dir):
            for file in files:
                if file.endswith(('.ts', '.py', '.json', '.sql')):
                    file_path = os.path.join(root, file)
                    node = self.build_file_node(file_path)
                    node_tokens = self.estimate_tokens(node)

                    if current_tokens + node_tokens > (self.max_tokens - 10000): # Reserve space for query
                        break

                    prompt_parts.append(node)
                    current_tokens += node_tokens

        prompt_parts.append("</context_zone>")

        # 3. Query at the bottom
        prompt_parts.append("<query>\n" + query + "\n</query>")

        return "\n".join(prompt_parts)

Trade-offs and Architectural Decisions

Using a 2-million-token context window is not always the correct architectural choice. Developers must weigh the trade-offs against Retrieval-Augmented Generation (RAG) and fine-tuning.

1. Latency: Processing 2 million tokens can result in Time-To-First-Token (TTFT) latencies of several seconds or even minutes, depending on the provider and infrastructure. For interactive applications, this is often unacceptable.
2. Cost: Input token costs scale linearly. Running a 2-million-token prompt for every user query is financially non-viable for high-throughput production systems.
3. Global Synthesis vs. Local Retrieval: RAG is highly efficient for retrieving specific, isolated facts. However, RAG fails when the task requires global synthesis, such as refactoring an entire codebase to use a new state management library. Large context windows excel at global synthesis because the entire state is present in the model's working memory.

Therefore, the decision framework should be:

• Use RAG for point-lookup queries and low-latency requirements.
• Use Large Context Windows for complex refactoring, architectural planning, and deep code analysis where global context is mandatory.

Mitigating Attention Degradation with Attention Anchors

To combat the "lost in the middle" effect within a 2-million-token context, we can employ "attention anchors." These are repetitive, high-level summaries placed at regular intervals throughout the prompt. For example, every 500,000 tokens, you can inject a structural map of the codebase. This reminds the model of the global architecture, reinforcing the attention weights on key components.

Another technique is "redundant schema definition." If your query relies heavily on a specific database schema, define that schema both in the static reference section and directly inside the query block at the bottom. This redundant placement ensures that the attention heads do not have to traverse the entire 2-million-token space to resolve basic structural questions.

Evaluating Retrieval Accuracy

Before deploying a large context prompt to production, you must measure its retrieval accuracy. Do not rely on generic benchmarks. Instead, implement a synthetic evaluation pipeline:

1. Generate synthetic needles: Create unique, random UUIDs associated with specific, arbitrary instructions (e.g., "If you see UUID-9823, append the word 'ALPHA' to the output").
2. Inject needles at varying depths: Place these synthetic needles at 10 percent, 30 percent, 50 percent, 70 percent, and 90 percent of your context window.
3. Run evaluations: Execute the prompt multiple times and measure the retrieval rate at each depth.
4. Optimize structure: If retrieval drops below 95 percent at the 50 percent depth, adjust your XML tagging, increase the redundancy of your anchors, or reduce the overall context size.

Conclusion

As context windows continue to expand, the bottleneck shifts from capacity to structure. Simply dumping data into a model is a recipe for high latency, high costs, and inaccurate outputs. By treating the context window as a structured memory space, using XML zoning, placing critical instructions at the boundaries, and programmatically assembling inputs, developers can maintain high retrieval accuracy even at the 2-million-token limit.