Golang The Series EP.155: Chunking Strategies for RAG Systems

Welcome to Golang The Series SS5: AI Awaken EP.155! In our previous episode, we successfully built a system to create collections on Qdrant. Now, imagine you have a 100-page employee handbook or a massive product manual that you want to integrate into a RAG (Retrieval-Augmented Generation) system.

A classic question that many AI development beginners ask is:

"Can we just convert this entire document into an embedding and feed it to the AI all at once?"

The short answer is "Absolutely not." Here are the two primary reasons why standard AI architectures cannot handle this approach:

• LLM Context Window Limits: Large Language Models (LLMs) have strict token quotas per request. Similarly, embedding models have hard limits on the amount of input text they can process to compute vector dimensions effectively.

• Diluted Meaning: Forcing a 10-to-20-page document into a single vector set dilutes the overall meaning. The fine-grained details and specific keywords get lost, making it impossible for the AI to pinpoint and retrieve exact information later on.

This is exactly why we need Chunking Strategies—the art of breaking down massive texts into smaller, bite-sized pieces that remain highly contextual.

In this article, we will dive deep into the most popular chunking methods and build a high-performance Chunking Engine using Go (Golang)!

Deep Dive: 3 Essential Text Chunking Methods for RAG

Chunking text for a RAG system isn't just about random slicing. It ranges from basic approaches to highly optimized techniques designed to help AI understand context perfectly. Generally, these are divided into 3 main levels:

Level 1: Character / Token-based Chunking (Fixed-Size Slicing)

This is the most basic method. You hardcode a specific limit for each chunk, such as 500 characters or tokens. Once the system hits that limit, it cuts the text right there.

• The Catch: This method often splits words or cuts sentences right down the middle. For non-English or complex languages, it can break mid-character or mid-word, immediately corrupting the semantic meaning when passed to an embedding model.

Level 2: Recursive Character Chunking (Structure-Aware Slicing)

A step up in intelligence, this method looks for "natural split points" within the document's structure. It prioritizes larger dividers like double newlines (\n\n), single newlines (\n), or spaces at the end of sentences. If a section is still too long after checking these markers, only then will it fall back to character counts. This approach does a much better job of keeping sentences and paragraphs intact.

Level 3: Sliding Window & Overlap (Contextual Bridges)

Regardless of which method you choose above, a crucial setting you cannot skip is Overlap. This involves pulling a portion of text from the end of the previous chunk and using it as the beginning of the next chunk.

Example: If you set a Chunk Size of 500 and an Overlap of 100, the last 100 characters of Chunk 1 will also become the first 100 characters of Chunk 2. This creates a contextual bridge, preventing critical data at the boundary from getting cut off.

Workshop: Implementing Sliding Window Chunking in Go

Let's write a Go function to handle text chunking based on characters (rune). Using rune ensures that multi-byte characters (like Thai, emojis, or special symbols) are counted and split accurately without causing data corruption.

Go

package main

import (
	"fmt"
)

// ChunkText splits text based on the specified size and overlap using runes.
func ChunkText(text string, chunkSize int, overlap int) []string {
	runes := []rune(text)
	var chunks []string

	// Prevent edge cases and infinite loops
	if chunkSize <= 0 {
		return chunks
	}
	if overlap >= chunkSize {
		overlap = chunkSize - 1 // Overlap must always be strictly less than Chunk Size
	}

	for i := 0; i < len(runes); {
		end := i + chunkSize
		if end > len(runes) {
			end = len(runes)
		}

		// Extract the chunk and append it to the slice
		chunks = append(chunks, string(runes[i:end]))

		// If we've reached the end of the text, break out of the loop
		if end == len(runes) {
			break
		}

		// Shift the index forward, factoring in the overlap to maintain context
		i += (chunkSize - overlap)
	}

	return chunks
}

func main() {
	longText := "Go is designed for excellent concurrency management. With features like Goroutines and Channels, " +
		"it can handle massive parallel processing workloads efficiently with minimal resource footprints. " +
		"This makes it highly suitable for building data pipelines in the era of AI-First architecture."

	// Slice into chunks of 50 characters with a 15-character overlap
	chunks := ChunkText(longText, 50, 15)

	for i, chunk := range chunks {
		fmt.Printf("🧩 Chunk %d: [%s]\n", i+1, chunk)
	}
}

Why Go is Perfect for Building Chunking Engines

As your RAG system scales, the volume of documents processed simultaneously can skyrocket (e.g., dozens of employees uploading enterprise manuals at the same time). This is where Go’s system-level performance shines:

• Goroutine Workers (Powerful Concurrency): You can accept massive text files and pass them into a Worker Pool of Goroutines to handle chunking concurrently. This prevents the main execution thread from blocking.

• Memory Efficiency: Go handles string slices and []rune with incredibly low memory overhead. You can easily process multi-megabyte text data streams without experiencing performance drops or worrying about memory leaks.

🎯 Daily Mission

Copy the source code above, run it locally, and observe the output. Check how the text overlaps between consecutive chunks to see the sliding window mechanism in action!

💡 Food for Thought: Based on the example code, if we want to change the logic from strict character counts to scanning for spaces or newlines (\n) so that words don't get cut in half, how would you modify the logic to find the end position? Give it a shot and upgrade the code!

❓ FAQ (Frequently Asked Questions)

What is the optimal Chunk Size and Overlap setting?

There is no one-size-fits-all answer. However, a good baseline for popular models like OpenAI's embeddings is a Chunk Size of 400–800 characters (runes) and an Overlap of 10%–20% of the chunk size (roughly 50–150 characters). This generally maintains a solid flow of context.

Why do we convert strings to a []rune slice before chunking in Go?

In Go, standard strings are evaluated by their size in bytes. While basic English characters take up 1 byte, special characters, emojis, and non-Latin scripts (like Thai or Japanese) require 3 to 4 bytes per character.

If you use standard string slicing, Go might cut right in the middle of a multi-byte character's byte sequence. This corrupts the character, rendering it as a broken symbol (). Converting the string to a []rune slice forces Go to treat indices as individual characters (code points), ensuring 100% accurate text slicing.

Conclusion: Better Chunking, Smarter AI 🎯

Building an accurate and intelligent RAG system isn't solely dependent on using the most advanced LLM; it starts with the quality of data you provide.

Implementing proper Chunking Strategies along with a well-tuned Overlap is a non-negotiable step in your data pipeline. Poorly chunked data leads to lost context and misinterpretations by the AI. By choosing Go to power your chunking engine, you guarantee that your infrastructure can scale and handle heavy document processing rapidly and efficiently.

In the Next Episode (EP.156) 🚀: Now that we have clean chunks saved into Qdrant, it's time to actually pull that data out and use it!

In our next episode, we will explore "Semantic Search: Hunting Content by Meaning." We'll look at how systems locate the most relevant chunk based on intent, moving past old-school keyword matching. See you in the next episode!

Follow Superdev Academy on all platforms:

• 🔵 Facebook: Superdev Academy Thailand

• 🎬 YouTube: Superdev Academy Channel

• 📸 Instagram: @superdevacademy

• 🎬 TikTok: @superdevacademy

• 🌐 Website: superdevacademy.com