What is Chunking in Prompt Engineering?
Explore how chunking can help you with 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.
| Chunking | Chaining | |
| Definition | Breaks down a large task or prompt into several smaller, more manageable pieces or ‘chunks’. Each chunk is carried out independently, and the final result is stitched together. | Uses the output of one prompt as the input for another. This creates a sequence or ‘chain’ of steps that fine-tunes and builds on previous outputs to reach a complex result. |
| How it works | Separates large prompts into smaller pieces Focuses on individual sections for more clarity Helps the AI process information step-by-step within a single prompt | Connects multiple prompts in a sequence Each prompt builds on the result of the previous Comes in handy for tasks where the outcome of one step informs the next |
| Use cases | Information breakdown Simplified instructions Data analysis | Complex problem solving Multi-step processes Narrative building |
| Task structure | Breaks a single task into smaller parts | Uses sequential prompts where the output informs the input |
| Prompt dependency | Each chunk can often be processed independently of each other | Prompts are interdependent, and later prompts rely heavily on earlier prompts |
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.
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, it’s easier to identify which chunk caused the issue. Debugging prompts that use chunking is a breeze compared to prompt chains, as you typically need to start from one end or the other and work your way through the entire 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.
