Arrays 101 for LLM Power Users — From Basics to “Token‑Efficient Thinking” (with C# Examples)

Arrays 101 for LLM Power Users — From Basics to “Token‑Efficient Thinking” (with C# Examples)

Arrays are one of those “basic” topics everyone thinks they already know… until they start building with LLMs.

Then suddenly you care about things like:

• Why does a slice allocate?
• How can I format data so the model “gets it” with fewer tokens?
• Why does chunking work better with fixed-size windows?
• How do arrays relate to vector embeddings, context windows, and retrieval?

In this post we’ll refresh core array fundamentals and then connect them to how you use LLMs effectively: prompt design, chunking strategies, memory layout intuition, and performance patterns in .NET.

You’ll leave with:

• A clean mental model of arrays (1D, 2D, jagged)
• Practical patterns (search, map, filter, reduce)
• The “LLM lens”: chunking, token budget, structured serialization
• Token-efficient formatting patterns (arrays → compact text)
• Performance + memory notes (cache friendliness, allocations, bounds checks)
• GitHub-ready C# examples and pro tips

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Table of Contents

• Mental Model: What an Array really is
• Array Basics in C#: creation, indexing, length
• 1D vs 2D vs Jagged: when to use which
• Traversal patterns: loops, foreach, Span
• Common algorithms: search, copy, resize, sort
• Arrays for LLM work: chunking, windows, embeddings prep
• Token-efficient formatting: arrays → compact text
• Performance notes: cache, bounds checks, allocations
• Production checklist

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1) Mental Model: What an Array really is

An array is:

A contiguous block of memory containing elements of the same type, accessible by index.

Key properties:

• Fixed size once created
• O(1) random access by index
• Strong memory locality → CPU cache friendly
• In .NET, T[] is a reference type object that contains:
  • an object header
  • the array length
  • the contiguous element region

When you index arr[i], the runtime does:

1) bounds check (unless optimized away)

2) address calculation: base + i * sizeof(T)

3) load/store

This is why arrays are extremely fast.

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2) Array Basics in C

2.1 Create arrays

int[] numbers = { 1, 2, 3, 25 };
string[] names = new[] { "Juan", "Luis", "Diana" };
var zeros = new int[10]; // fixed length, initialized to 0

2.2 Indexing and length

Console.WriteLine(numbers[0]);     // 1
Console.WriteLine(numbers.Length); // 4
numbers[1] = 200;                  // update

2.3 Bounds checks

// throws IndexOutOfRangeException
// Console.WriteLine(numbers[999]);

Modern JIT can remove bounds checks in tight loops, but only when it can prove safety.

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3) 1D vs 2D vs Jagged Arrays (and why it matters)

3.1 One-dimensional (T[])

Best default: compact, fast, simple.

3.2 Rectangular multi-dimensional (T[,])

True 2D grid:

int[,] grid = new int[3, 4];
grid[1, 2] = 99;

Tradeoff: indexing costs can be higher than T[] because the runtime must compute offsets for multiple dimensions.

3.3 Jagged (T[][])

Array of arrays:

int[][] jagged = new int[3][];
jagged[0] = new[] { 1, 2 };
jagged[1] = new[] { 3, 4, 5 };
jagged[2] = new[] { 6 };

Tradeoff: not contiguous across rows, but often faster and more flexible than [,] for “rows of variable length”.

LLM note: jagged arrays map well to “chunks of chunks”: document → paragraphs → sentences.

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4) Traversal Patterns

4.1 for loop (fastest control)

for (int i = 0; i < numbers.Length; i++)
{
    Console.WriteLine(numbers[i]);
}

4.2 foreach (clean)

foreach (var n in numbers)
{
    Console.WriteLine(n);
}

4.3 Span: “array view” without allocations

Span<int> window = numbers.AsSpan(1, 2); // slice [numbers[1], numbers[2]]
window[0] = 777;

This is gold for performance and for chunking windows (more on that soon).

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5) Common Array Algorithms (you’ll use these everywhere)

5.1 Linear search (O(n))

static int IndexOf<T>(T[] arr, T value)
{
    for (int i = 0; i < arr.Length; i++)
        if (EqualityComparer<T>.Default.Equals(arr[i], value))
            return i;

    return -1;
}

5.2 Copy

var copy = new int[numbers.Length];
Array.Copy(numbers, copy, numbers.Length);

5.3 Sort

Array.Sort(numbers);

5.4 Resize (allocates new array)

Array.Resize(ref numbers, numbers.Length + 10);

Arrays are fixed-size. Resizing means: allocate new → copy → replace reference.

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6) Arrays for LLM Work: Chunking, Sliding Windows, and Token Budgets

Here’s the big unlock: LLMs operate over sequences (tokens). Arrays are your tool for structuring sequences before you serialize to prompts.

6.1 Chunking documents into arrays

When you chunk a document for RAG:

• You split the text into segments (chunks)
• Each chunk becomes an element in string[] chunks
• You embed those chunks into vectors

Why arrays? Because they give you:

• deterministic order
• stable indices / IDs
• fast slicing into windows

Example:

string[] chunks = ChunkByChars(text, chunkSize: 1200, overlap: 200);

6.2 Sliding window (overlap) is literally array slicing

LLMs need overlap to keep context continuity:

• chunk 0: chars 0..1200
• chunk 1: chars 1000..2200
• chunk 2: chars 2000..3200

That overlap is a window concept — which maps directly to Span<T> windows.

6.3 Chunking is a “locality” strategy

Chunking works because:

• you reduce irrelevant noise
• you feed the model compact, relevant context
• you stay within context window limits

Arrays make chunking reproducible, testable, and performant.

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7) Token‑Efficient Formatting: Arrays → Compact Text That Models Understand

Most people waste tokens by dumping unstructured text.

Instead, treat your prompt like a compact serialization problem.

7.1 Bad: long prose

“Here is a bunch of data, please analyze it…”

7.2 Better: compact array-of-records

If you have call records, store them in an array of small lines:

var lines = new[]
{
    "t=2025-12-10; dir=in; dur=19.0; skill=",
    "t=2025-12-10; dir=out; dur=40.2; skill=sales"
};

Then in prompt:

Records:
[0] t=2025-12-10; dir=in; dur=19.0; skill=
[1] t=2025-12-10; dir=out; dur=40.2; skill=sales
Task: summarize anomalies, compute avg duration, list missing skills.

This works because:

• the model sees consistent structure
• tokens are minimized
• indices are stable → you can reference records by [i]

7.3 A practical “LLM-ready array serializer”

static string ToLLMLines(string[] items, string header = "Items:")
{
    var sb = new System.Text.StringBuilder();
    sb.AppendLine(header);
    for (int i = 0; i < items.Length; i++)
        sb.AppendLine($"[{i}] {items[i]}");
    return sb.ToString();
}

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8) Performance Notes That Actually Matter

8.1 CPU cache friendliness

Arrays are contiguous → better cache locality → faster loops.

This matters when you:

• compute embeddings
• pre-process large corpora
• normalize tokens
• batch requests

8.2 Allocation awareness

• Array.Resize allocates
• string.Split allocates
• LINQ can allocate (enumerators, closures, intermediate collections)

For high-volume pipelines, prefer:

• Span<T> / ReadOnlySpan<T>
• ArrayPool<T>
• simple loops

8.3 Bounds-check elimination

JIT can remove bounds checks in patterns like:

for (int i = 0; i < arr.Length; i++) { /* arr[i] */ }

But if you index with non-obvious expressions, the JIT may keep checks.

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9) A GitHub‑Ready C# Deep Dive Example (Arrays + “LLM Thinking”)

Below is a compact example you can drop into a repo to demonstrate:

• arrays
• chunking
• overlap
• serialization for prompts

using System;
using System.Collections.Generic;
using System.Text;

public static class LlmArrayPatterns
{
    public static void Demo()
    {
        string text = new string('A', 3000); // pretend this is a big document
        var chunks = ChunkByChars(text, chunkSize: 1200, overlap: 200);

        Console.WriteLine($"Chunks: {chunks.Length}");
        Console.WriteLine(ToLLMLines(chunks, header: "Chunk Preview:"));
    }

    // Basic chunker: (size, overlap) -> array of chunks.
    public static string[] ChunkByChars(string input, int chunkSize, int overlap)
    {
        if (chunkSize <= 0) throw new ArgumentOutOfRangeException(nameof(chunkSize));
        if (overlap < 0 || overlap >= chunkSize) throw new ArgumentOutOfRangeException(nameof(overlap));

        var list = new List<string>();
        int step = chunkSize - overlap;

        for (int start = 0; start < input.Length; start += step)
        {
            int len = Math.Min(chunkSize, input.Length - start);
            list.Add(input.Substring(start, len));
            if (start + len >= input.Length) break;
        }

        return list.ToArray();
    }

    // Prompt-friendly formatter: stable indices.
    public static string ToLLMLines(string[] items, string header = "Items:")
    {
        var sb = new StringBuilder();
        sb.AppendLine(header);

        for (int i = 0; i < items.Length; i++)
        {
            var preview = items[i].Length > 40 ? items[i].Substring(0, 40) + "..." : items[i];
            sb.AppendLine($"[{i}] len={items[i].Length} :: {preview}");
        }

        return sb.ToString();
    }
}

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10) Production Checklist

Before you build an LLM pipeline with “arrays everywhere”:

• [ ] Keep raw text chunks in string[] or List<string> (then freeze to array)
• [ ] Use consistent chunk size + overlap
• [ ] Preserve indices / IDs for traceability
• [ ] Serialize arrays into token-efficient formats (indexed lines)
• [ ] Avoid unnecessary allocations in hot paths (use Span, pooling)
• [ ] Write tests: chunk boundaries, overlap correctness, stable ordering

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Wrap‑up

Arrays are the simplest data structure — but they’re also:

• the foundation of fast memory access
• the mental model behind windows and chunking
• the backbone of token-efficient prompt inputs

If you want, I can also generate:

• A full .NET 8 console repo with these demos
• A BenchmarkDotNet suite (arrays vs LINQ vs Span)
• A “RAG chunking spec” template for your team docs

Happy building — and may your prompts be compact and your arrays cache-friendly. 🧠⚡

Comments

[shemith mohanan]

Loved how you connected basic array concepts to real LLM use cases like chunking, token efficiency, and prompt structuring. The Span + sliding window explanation was especially practical. Great bridge between fundamentals and modern AI workflows.