According to a 2024 study titled ‘Lost in the Middle: How Language Models Use Long Contexts’, increasing the number of retrieved documents doesn’t guarantee that an AI model will make better use of a reading pile. The study looked at models GPT-3.5-Turbo and Claude-1.3 retrieving 20 to 50 documents at once. Although these two models looked at more information, the answer performance improved by only about 1% to 1.5%. That’s dismal.
It’s also raising questions about relying on oversized prompts and supports exploring more controlled approaches, such as chaining. For content teams, the findings suggest that mega-prompting may not be earning the hype it receives in some content circles, and that a pivot towards chain prompting may better suit content teams.
Today, we’ll explore mega-prompts vs chaining to help you understand what each is capable of and where these methods fall short. We’ll also share a peek at the team at Better Content Matter’s playbook for long-form content that doesn’t lose the plot. Spoiler alert…it includes mega-prompting and chaining.
The Difference Between Mega-Prompts and Prompt Chaining
The term mega-prompt is informal in nature and is wording you might use to describe an AI model being fed one large instruction to complete most of an assignment in a single response.
For content creation, it begins with the intended audience, followed by research, and finalized by the tone requirements. These, of course, are also dependent on the outline and word count squeezed in somewhere before the request to write. On the other hand, prompt chaining divides the content assignment into piecemeal stages, each fed to an AI model.
With this method, the output from one prompt informs the next, creating a nifty workflow rather than one oversized request. According to Correlation One, this method can be thought of as taking an end goal and breaking it into multi-step tasks, using prompts to achieve it.
To put it into perspective, for a long-form article, that chain might look like this:
- Research notes
- Approved outline
- Section drafts
- Full article edits
Hopefully, the above didn’t bore you, but essentially, chaining adds a checkpoint between major decisions. This, in theory, can help rather than harm content creation.
Where Mega-Prompts Help and Where They Fall Short
When you have a small task or something uber contained, then a mega-prompt could be helpful. You could probably get away with using a mega-prompt to rewrite a short passage or generate headline options.
You may also have good results using one to brainstorm topic ideas or do a quick edit once over. Or better yet, serve as a master instruction sheet for a project because the central rules remain in place. However, the trouble begins when all possible requirements are packed into a single prompt text box. It is at this point that the prompt begins to resemble an overstuffed suitcase with someone sitting on the lid…no, really.
Often, with a mega-prompt, you’ll find that sourcing rules are buried halfway through, while the intended reader fades from view once the model reaches later sections. This usually means content isn’t being written properly for client audiences and that sourcing may be inaccurate.
What makes this situation even more of a headache is that, when issues like this pop up, a writer has to try to trace the problem through the full response to find where things started going astray. This wastes time and money and can lead to harmful delays in the content process.
Why Chaining Is Often Better for Long Form Content
Chaining is usually better for long-form content because it’s far easier to manage. Each prompt has a special purpose, while the writer remains firmly in control of what moves forward. With chaining, a writer can pause between major stages, allowing any problems to be caught and addressed as the writing develops rather than at the end. This naturally gives you room to check the direction to ensure your long-form content doesn’t lose the plot.
For example, through chaining, you can remove unsupported claims before they enter your outline or prevent two sections from making the same point before hundreds of words are written around them. If you’re unsure whether chaining is the right method, consider it when a long-form task contains several instructions or requires multiple transformations.
Where Prompt Chaining Can Go Wrong
Although chaining is a better method for long-form content, it doesn’t mean that the process is foolproof. For instance, one risk of chain prompting is that an error in research notes can still find its way into the final draft. Another risk is that context can disappear between prompts if only the last output is carried forward. That context is the meat your article needs.
That’s why it’s often recommended that a writer include relevant information from earlier stages rather than assuming the model will remember the original goal because…it’s just not that smart. Additionally, a writer should avoid creating numerous tiny steps to the point that the chain becomes harder to manage than the task itself.
Moreover, additional prompts can lead to more model calls, which may increase processing time or API costs in automated workflows. This tradeoff is far less for a writer manually producing one blog, because they will have more chances to catch a problem before it travels into an article.
However, it becomes significantly more noticeable when a chain runs repeatedly at scale, as is the case with businesses and agencies producing content, because token usage can increase drastically.
Inside Our Writers’ Playbook for Long-Form Content
Better Content Matters uses a combination of mega-prompts and chain prompts to produce SEO and AIO-friendly content for clients.
Our writers use a mega-prompt as the master instruction sheet for long-form content. This is done to preserve the intended reader and an article’s central argument. In this mega-prompt, the source requirements remain alongside voice rules and prohibited phrasing.
What then moves to the chain prompt is most of the production work
- Topic research. The first prompt handily gathers relevant information from credible sources, so the article begins with verifiable material. The last thing you want is factual inaccuracies, as that’s the fastest way to lose a client when their reputation is damaged.
- Research notes review. Next, a writer uses a second prompt to check each claim against its original source and removes anything that doesn’t support the article’s main argument. This is more important than you know.
- Outline creation: Once the research is approved, the next prompt assigns a purpose to each section and arranges the ideas in an order that advances discussion. If you don’t do this, your article is likely to talk in circles and go nowhere fast.
- Section drafting: A writer will next use a few prompts to draft sections of an article, and each will carry the main claim and briefly note what the previous section established. This helps the next passage continue the discussion rather than begin again, which you don’t want.
- Editing of the completed article: The finished sections are compiled into a single article, where a writer checks for repetition, fixes any content gaps, rechecks factual claims (for good measure), and ensures the content meets the goal and client expectations.
The Wrap-Up on Mega-Prompts Vs Chaining
So now you have tea on mega-prompts vs chaining and why one (cough cough) offers far more value than the other. However, you can also clearly see that a mix of mega-prompting and chaining is usually the sweet spot for content creation, regardless of industry. By mixing both, you can keep your main instruction together and have ample opportunities to review writing to keep it on track, ensuring what a client receives is the best possible output.
But, understandably, utilizing mega-prompts and chaining oneself can be a little tricky. Fortunately, Better Content Matters combines the two approaches where it is most useful, with professional writers and editors guiding the process. If you would like to discuss long-form content that uses AI without compromising the article’s purpose, contact the team here. They are a team you’ll want on your side when it comes to content creation and utilizing AI.
