What is Chunking in Prompt Engineering?

Ever wanted ChatGPT to do something complex but didn’t like the results?

Next time, give chunking a try.

Chunking is a simple and useful prompting technique that helps generative AI understand complex tasks so it can provide higher-quality responses.

In this blog, we’ll explore how chunking works, its benefits, and why it’s useful.

What is Chunking?

In prompt engineering, chunking refers to the process of dividing a large, complex task (or prompt) into smaller pieces, which are called “chunks”.

This is done because generative AI models like ChatGPT and alternative LLMs don’t perform well with large prompts. Too much information overwhelms them, leading to inaccurate outputs, which can be a dealbreaker if you’re trying to distill an LLM to create a smaller model.

Instead, breaking large prompts into smaller chunks helps AI models work with the information more effectively, leading to more accurate results.

Chunking vs Chaining

Another technique for getting generative AI to work on complex tasks is prompt chaining. Chunking and chaining are both useful, but they serve different purposes in generative AI.

Here’s a brief overview of chunking versus chaining.

Ultimately, both chunking and chaining help AI models process large tasks more effectively and enable better outputs. They also save AI models from information overload, and you can repurpose identical chunks by caching them to cut AI costs.

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Benefits of Chunking in Prompt Engineering

These are some of the benefits of using chunking when creating AI prompts:

• Increased flexibility – Chunking makes it easier for you to adjust prompts. If one chunk needs refinement, you can modify it without affecting the entire prompt, which is something you can’t do with chaining.
• Easier debugging – If an output doesn’t meet your expectations, chunking makes it easier to identify which chunk caused the issue. It’s also simpler to debug prompts that use chunking instead of prompt chains, which require you to work from the start all the way through the sequence.
• Scalability – Chunking allows you to scale task complexity up and down by adding or removing chunks as needed. You won’t ruin the entire prompt sequence.
• Greater focus – Chunks target specific elements of a task or question. This lets the AI concentrate on giving information solely for the prompt you entered.

Challenges of Chunking in Prompt Engineering

Just like prompt chaining, chunking comes with its fair share of drawbacks and pitfalls:

• Context loss – When breaking down lots of information into chunks, you run the risk that AI may lose the overall context and lead you to a less than desirable result.
• Increased complexity – Managing several small chunks complicates the prompt engineering process as you need to check that each chunk aligns with the others.
• Overchunking – While breaking a huge prompt into smaller parts improves output accuracy, it’s possible to divide the prompt into too many. This might dilute the focus and generate responses that lack sufficient depth.
• Interdependencies – Chunks may or may not rely on other chunks. If you don’t manage the chunks correctly, you might complicate your overall prompt engineering.

Best Practices for Chunking

If you want to try out prompt chunking, here are some best practices to help you get the outputs you want:

• Maintain context – Each chunk needs to retain enough context that the chunk makes sense on its own and fits into the larger task.
• Limit chunk size – Chunks are useful because they break big prompts into bite-sized parts. You’ll need to keep chunks concise to avoid overloading the AI.
• Logical organization – Although chunking allows you to process parts independently and without complete context, it’s still best to arrange chunks in a logical order, such as progressing from general concepts to specific details.
• Use clear language – As with any generative AI prompt, your language needs to be short, clear, and to the point. This reduces ambiguity and allows AI to focus on fulfilling your request.
• Limit dependencies and group related information – Try to keep related concepts inside the same chunk. This lets AI maintain coherence and relevance to your task while minimizing the reliance of one chunk on another.

Test, test, and test – Last (and certainly not least), experiment with different chunk sizes, structures, and organization to see what works best for you.

Practical case: summarizing a large text using chunking

Task: A marketer wants to get a concise report from a 10,000-word article for an email newsletter. Submitting the entire text to AI leads to errors, omissions, and information loss.

Solution: Semantic Chunking + Summary Composition

Steps:

1. Divide the text into logical blocks (chunks)
   The article is split into 5 sections (~2,000 words each) according to content:
   Introduction
   Methodology
   Results
   Analysis
   Conclusions
   
2. Apply separate prompts to each chunk
   For each block, the prompt is:
   “Summarize the following section in 3–4 sentences, focusing on key insights.”
   
3. Form the final summary
   All 5 summaries are combined with the prompt:
   “Combine the following summaries into a concise executive summary (under 300 words) suitable for a C-level email.”
   

Verify
Check for consistency with the tone and key messages of the original document.

FAQ

What chunk size works best?

Chunks should be concise enough to avoid overloading the AI while still retaining the context needed for the task. Keeping them small and focused improves clarity and accuracy.

How do I manage overlapping contexts?

Each chunk should include enough relevant information so it makes sense on its own and still fits into the larger task. Group related concepts within the same chunk to maintain continuity.

When should I use semantic vs fixed-size chunks?

Semantic chunks work well when the content can be divided into logical sections or related topics. Fixed-size chunks are useful for managing large amounts of text where a consistent size helps maintain processing efficiency.