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One of the first use cases I tried with an LLM was generating content. The prompt was probably something along the lines of “write a blog article about {{topic}}”. Needless to say, the output wasn’t great. But even with just a little bit of prompt engineering, my outputs got a lot better.

Over the past year, we’ve written about tons of different ways to get better outputs from LLMs, focusing on prompt engineering methods and prompt patterns. Today we’ll flip that around and focus on the type of task, rather than the method.

Specifically, we are going to focus on prompt engineering for content creation when interacting with LLMs via an API, rather than a web interface like ChatGPT.

Additionally, we pulled insights from people who actually use these LLMs in production environments. i.e., the advice here will be extremely practical and based on people's actual experience trying to refine prompts for content creation.

Principles of prompt engineering for content creation

We’ll start with some basic best practices/tips and make our way to more advanced methods.

We'll start with a basic prompt, "write a LinkedIn post about prompt engineering" and refine it using various prompt engineering principles.

Principle #1: Prompt Structure and Clarity

Original Prompt: "Write a LinkedIn post about prompt engineering."

Refined Prompt: "Write a LinkedIn post about the key benefits of prompt engineering for AI content creation. Aim it at professionals in the AI industry. The post should be engaging and informative, with a call to action encouraging readers to explore prompt engineering techniques."

Prompt structure is extremely important. Here’s how Stefan Keranov, the Co-founder and engineering leader at Mindstone, structures his content generation prompts:

Principle #2: Specificity and Information

Original Prompt: "Write a LinkedIn post about prompt engineering."

Refined Prompt: "Write a LinkedIn post about the key benefits of prompt engineering for AI content creation, aimed at professionals in the AI industry. Include three specific benefits and a real-world example of how prompt engineering improved content quality. The post should be engaging and informative, with a call to action encouraging readers to explore prompt engineering techniques."

No matter what prompt you're working on, the more specific you can be upfront, the better the model can follow your instructions.

Principle #3: Use of Affirmative Directives

Original Prompt: "Write a LinkedIn post about prompt engineering."

Refined Prompt:"Create an engaging and informative LinkedIn post about the key benefits of prompt engineering for AI content creation. Highlight three specific benefits and provide a real-world example of success. End with a call to action urging professionals in the AI industry to explore prompt engineering techniques."

Telling the model what to do, rather than what not to do, helps to guide the model to a desired outcome. It's one of the top prompt engineering best practices from OpenAI.

Principle #4: Incorporating Examples

Original Prompt: "Write a LinkedIn post about prompt engineering."

Refined Prompt: "Write a LinkedIn post about the key benefits of prompt engineering for AI content creation. Include three specific benefits and a real-world example of how prompt engineering improved content quality. Make the post engaging and informative, with a call to action encouraging readers to explore prompt engineering techniques. Here is an example of a recent post that performed well: {{Example_LinkedIn_Post}}"

Using examples in your prompt, also known as few-shot learning, may be the most effective and efficient method to get better outputs from LLMs, regardless of the task. For more info on few shot prompting, check out our guide: The Few Shot Prompting Guide.

Erich Hellstrom, founder of PromptPerfect, saves examples of well-performing content to inject into his prompt templates, ensuring the outputs mimic the desired tone and structure.

Principle #5: Role Assignment (Persona)

Original Prompt: "Write a LinkedIn post about prompt engineering."

Refined Prompt: "Act as a prompt engineer and write a LinkedIn post about the key benefits of prompt engineering for AI content creation. Highlight three specific benefits and provide a real-world example of success. Ensure the tone is engaging and motivational, ending with a call to action for AI professionals."

Tailoring the AI’s tone and style to a specific role or audience increases the specificity and relatability of the content.

Next, we’ll take a look at a few prompt patterns that can be applied when prompt engineering for content creation. Prompt patterns are high-level methods that provide reusable, structured solutions to overcome common LLM output problems. For more information about prompt patterns, check out our guide here: Prompt Patterns: What They Are and 16 You Should Know

Hey everyone, how's it going? This is Dan here from PromptHub. Welcome back to the channel. Today we're going to be focusing on a specific type of task you can use an LLM for, which is a pretty popular one, and that's generating content and how you can get better outputs from LLMs when you're doing this task.

I think one of the first tasks I tried to do with ChatGPT was content creation. It's generating text, so it makes sense an LLM would be pretty good at that. It was a low-hanging fruit thing to try out to see how good the models are. My first prompt was probably something along these lines: "Write a blog article about X." You can probably guess that the output I got back wasn't very great.

After a little bit of prompt engineering and just tweaking and iterating, you can get something that's pretty good. Although, there are still a ton of challenges to overcome, such as the output sounding too much like AI, being too lengthy, hallucinations, and other issues. I think a fair amount of them can be dealt with via engineering.

So we're going to look at a bunch of different things today. Let me make myself small. There we go. We'll look at general principles, some patterns, more advanced prompt engineering methods, a direct look at some of these challenges and solutions, and parameters and how to think about evaluating these outputs.

We'll go a little quick through these earlier sections and use this prompt as our base prompt that we will enhance: "Write a LinkedIn post about prompt engineering."

Principle number one, this goes for any type of task, is structure and clarity. Give clear directions about what you want this post to be about. For example, talk about the key benefits of prompt engineering and aim it at professionals in the AI industry. This simple update to the prompt will get you much better and more engaging outputs.

Specificity and information: Be more specific about the output style. For example, you might want three specific benefits and a real-world example. This helps the model give you an output that you're looking for. The model can't read your mind, so by being specific, you will get better outputs.

Use affirmative directives: This means don't use negative directives like "don't do this" or "don't do that." Instead, say, "I want you to do this" or "I want you to do that." This is one of the first pieces of guidance we got from OpenAI on prompt engineering, and it has been effective based on my tests. I've seen people writing prompts with negative directives, and that seems to work for them as well, so it's worth testing.

Incorporating examples: Also known as few-shot prompting or few-shot learning, showing an example of a good piece of content is very helpful. This is one of the first tips we give to teams when they have trouble with any type of prompt, especially content generation. We have a guide on this that I'll link below.

Using a persona: This helps steer the model in the right direction. For example, for a post about prompt engineering, the LLM could go technical or more general. By telling it to act as a prompt engineer, we've steered it in the direction we want, which is probably in between those two.

Next, we'll look at some prompt patterns for content generation. A prompt pattern is a higher-level solution for specific problems, abstracted and used across many use cases, including content generation. One pattern is the template pattern, where you give the model a template, such as an introduction, key benefits section, real-world example, and call to action.

Another pattern is the reflection pattern, telling the model to reflect on its output and make iterative suggestions or improvements. This can be helpful for generating content or logical reasoning.

Now, I'll sprint through some advanced engineering methods. These are lower-level, actionable prompts you can implement right away.

Multi-persona prompting: Prompt the model to simulate a brainstorming process with multiple experts. For a LinkedIn post on prompt engineering, the personas could be a LinkedIn specialist, a prompt engineer, and a writer. These perspectives lead to more engaging outputs.

According to prompting: Add "according to [source]" to your prompt to ground the model's response in a specific source, reducing hallucinations and increasing accuracy. For example, "What part of the digestive tube do you expect the initial digestion of starch to occur in, according to Wikipedia?"

Emotion prompting: Use emotional stimuli in your prompt to get better outputs from the LLM. Research has shown that this approach can improve accuracy by 8-12%.

Chain of density prompting: Have the model write a summary, review it, check for missing pieces or entities, and refine it iteratively. This ensures the content is informative and concise, starting with a short summary and adding to it in small pieces.

Teams often encounter four main issues with content generation: outputs sounding too much like AI, being too lengthy, maintaining context and continuity, and hallucinations.

For AI-sounding outputs, use style anchoring by pointing to a specific writing style to emulate, such as your own. Few-shot prompting is also effective by showing examples of how you write.

For lengthy outputs, few-shot prompting helps by showing examples of the desired length. Skeleton of thought prompting creates an outline and fills it in, ensuring a continuous information stream. Chain of density prompting also helps by iteratively refining the content.

To handle hallucinations, use according to prompting, implement a second LLM for fact-checking, or other prompt patterns we’ll link below. Human-in-the-loop review is also useful for production environments.

Lastly, we'll look at some parameters. The most important parameter to keep in mind is temperature, which influences how deterministic or creative the model will be. The default value is 1 for both OpenAI and Anthropic, but they have slightly different ranges. Testing temperature can help with many challenges.

Max tokens determine the maximum length the LLM can generate. If the content is too long, tweak this parameter. Frequency and presence penalties prevent the model from repeating itself.

Here are some example values you can screenshot. Lastly, a five-step process to evaluate outputs: model selection, tweaking, prompt iteration, testing with different data, and team involvement. This helps ensure you get the best outputs.

That's it for today. If you have any questions or comments, feel free to drop them below. See you!

Prompt patterns for content generation prompts

Prompt Pattern #1: Template Pattern

Send a specific template format that you want the LLM to follow when it produces an output.

Example Prompt:

"Provide a LinkedIn post about the key benefits of prompt engineering for AI content creation, with sections:
Key Benefits:
Real-World Example:
Call to Action:"

Prompt Pattern #2: Reflection Pattern

Prompt the LLM to introspect and suggest improvements after generating the first draft..

Example Prompt Refinement:"Write a LinkedIn post about prompt engineering and then reflect on the output to suggest improvements."

Advanced prompt engineering methods for content creation

Next up, we’ll take a look at some more advanced prompt engineering techniques that you can add to your tool belt to generate better content. All of these methods have free templates you can copy, use, save etc.

Multi-Persona Prompting

Multi-Persona Prompting involves the LLM identifying multiple personas to collaboratively work on the task at hand.

For example, going back to our original prompt "Write a LinkedIn post about prompt engineering", the LLM may identify a prompt engineer, a social media marketer, and a LinkedIn influencer to collaboratively work on crafting the post.

This prompt is really fun in general, as you can see the different personas working together.

By simulating a group of experts, the AI can produce richer, more nuanced outputs.

Comparing Multi persona prompting flow to CoT
Comparing normal and chain of thought prompt to multi-persona prompting

How It Helps:

  • Diverse Perspectives: Incorporating different viewpoints can lead to more comprehensive and balanced content.
  • Engagement: A variety of voices can make the content more engaging and relatable.

Multi-person prompt template in PromptHub

"According to" Prompting

One of our favorite prompt engineering methods, due to its simplicity, this technique involves grounding the AI’s responses in specific, reliable sources, which can help improve accuracy and reduce hallucinations.

For more info on this method, check out our blog post here: Improve accuracy and reduce hallucinations with a simple prompting technique.

Example Prompt : "Write a LinkedIn post about prompt engineering, citing information according to the PromptHub blog."

Messages between human and AI, using the according to prompting  method

How It Helps:

  • Accuracy: Grounding responses in reliable sources reduces hallucinations.
  • Credibility: Citing reputable sources enhances the credibility of the content.

The "according to" prompt template in the PromptHub platform


LLMs, having been trained on human data, have a funny way of holding up a mirror to us. EmotionPrompt is an example of this. By incorporating emotional stimuli into prompts, we can get better and more accurate outputs.

Example Prompt: "Write a LinkedIn post about the key benefits of prompt engineering for AI content creation; this is crucial for my job"

Two prompts comparing a normal prompt with EmotionPrompt

How It Helps:

  • Increased accuracy: Emotional language has proven to result in more accurate outputs

The EmotionPrompt template in the PromptHub platform

Chain of Density (CoD) Prompting

Chain of density prompting aims to improve summaries by iteratively integrating relevant entities into the summary, balancing detail and brevity.

How It Helps:

  • Brevity with Depth: Ensures the content is both informative and succinct.
  • Readability: Helps in producing summaries that are easy to read and understand.

Chain of density prompt template in the PromptHub platform

Real world challenges and solutions

Even with all the tips, templates, and methods we've covered, there are tons of hurdles to overcome when writing prompts for content generation. From 'sounding like AI' to outputs that are just too lengthy, there are many challenges to overcome.

Let's look at some challenges and solutions from people working in the field.

Peter Gostev, head of AI at Moonpig and a great follow on LinkedIn, says the most common challenge he faces is “the LLMs change the style too much, making it generic.”

A common problem, Peter spun up a pretty cool custom GPT called “Slight Re-Writer”, which has the model spell out the amount that it can change the content, helping to keep the model from changing too much and making the text sound like AI.

Erich Hellstrom, founder of PromptPerfect, mentions that brevity is one of the bigger challenges he runs into. This feeling seems to becoming more popular with GPT-4o.

Erich mentions, “The best way I’ve found to overcome overly long outputs is to prompt the LLM to write based on examples, or to iteratively tell it how to edit the content after it first generates it, with specific things to remove.”

Here's one of Erich’s content generation prompts:

Stefan Keranov, Co-founder at Mindstone, is constantly shipping content generation prompts to production for the courses they run. Unsurprisingly, he notes that the biggest issues they bump up against are related to hallucinations. Stefan and the team at Mindstone elect to have a human-in-the-loop for any course content that isn’t generated on the fly.

For more help on reducing hallucinations, check out our blog post: Three Prompt Engineering Methods to Reduce Hallucinations

Danai Myrtzani, a prompt engineer at Sleed, notes a few common challenges. One of them is maintaining context and continuity when generating longer pieces, like white papers. One prompt engineering method to reach for in those situations is Skeleton of Thought Prompting.

Additionally, Danai spends a lot of time generating content in languages other than English, like Greek. She notes that Google’s Gemini tends to do a better job at this compared to OpenAI’s models, but generating content in Greek still requires a lot of post-editing compared to generating content in English.

Here's a prompt Danai uses often for writing content:

Parameters for content generation prompts

Parameters are LLM settings you can adjust to affect the outputs generated. For more info on Anthropic's and OpenAI's model parameters, check out our guides:

We'll go over the most important parameters, and some guidelines about what values work best for content generation.


Temperature influences how deterministic the response from the model will be. The lower the temperature, the more deterministic. The higher the temperature, the more creative and chaotic the response will be.

For OpenAI models, temperature can be between 0 and 2. Higher values (0.8) will make outputs more random and creative. Lower values will make them more deterministic.

For Anthropic models, temperature can be between 0 and 1.

For both providers, the default value is 1.

Some values to test:

  • Creative Tasks (e.g., blog posts, social media content): 0.7 to 0.9
  • Factual Tasks (e.g., technical writing): 0.2 to 0.5

Top-p (Nucleus Sampling)

Top-p, similar to temperature, influences the creativity of the model’s responses. It limits the model to consider only the most probable tokens until the cumulative probability reaches the chosen threshold.

For example a top-p value of 0.5 means only the tokens comprising the top 50% probability mass are considered.

It’s recommended to adjust either temperature or top-p, but not both. In practice, we see more teams use temperature rather than top p.

For both OpenAI and Anthropic, top-p defaults to a value of 1 and can range between 0 and 1.

Some values to test:

  • Recommended Values: 0.85 to 0.95 to balance creativity and coherence.

Max Tokens

Max tokens determines the maximum length of the output in tokens generated by the model. It can stop before that value, but it will not exceed it.

So if the model is producing more content than you'd like, you may want to try out using the max tokens parameter to constrain it.

Some values to test:

  • Short Content (e.g., Tweets): 50-100 tokens.
  • Medium Content (e.g., LinkedIn posts): 150-250 tokens.
  • Long Content (e.g., Blog posts): 500-1000 tokens.

Frequency Penalty

The frequency penalty parameter reduces the likelihood of repeated phrases or words by penalizing frequent tokens. This decreases the likelihood that the model repeats something verbatim.

It is an optional parameter, defaults to 0, and can range between -2.0 and 2.0. The higher the number the larger the penalty.

This is one of the more finicky parameters. For example, the OpenAI documentation mentions the value can be between -2.0 and 2.0, but in their own playground, you can’t set the value to anything below 0. Anthropic doesn’t offer this parameter.

Our recommendation would be to leave this value at the default of 0. If you’re having problems with repetitive content, a good place to start testing this parameter would be around 0.5.

Presence Penalty

Very similar to frequency penalty and the same guidance applies here.

Stop Sequences

Stop sequences are text sequences that will cause the model to stop generating text.

  • Example: Use a stop sequence like "\n\n" to end a response neatly.


  • Parameter Settings for Different Scenarios:
    • Creative LinkedIn Post:
      • Temperature: 0.8
      • Max Tokens: 200
      • Top-p: 1 (default)
    • Technical Blog Post:
      • Temperature: 0.4
      • Max Tokens: 800
      • Top-p: 1 (default)

How to evaluate outputs?

When it comes to content generation prompts, evaluating output quality is much more art than science. Frankly, the best method here is human review. Here are a couple of ways make this more process more systematic.

Step 1: Model Selection

If you already know which model you want to use, great. But it may be worth revisiting, as LLMs can drift and change over time.

Test an initial version of your prompt across a variety of models to see how the outputs look. You're not looking for perfect outputs, but generally which model is producing the highest quality output. Look for things like structure, tone, and length.

Batch testing in PromptHub is one way to do this very easily.

2 prompt chat templates side-by-side
Side-by-side chat testing, Anthropic vs OpenAI

Step 2: Parameter Tweaks

Okay, we’ve got our model set; now let’s turn to the parameters. We gave some broad guidelines above, but you’ll want to do some direct testing yourself as each use case differs.

The first parameter to test, and arguably the most important, is temperature. You’ll want to test your prompt across the same model but vary the temperature. This will allow you to see how the outputs change in style as the temperature changes.

Step 3: Prompt Iteration

Now you’ll want to start testing different versions of the prompts. You can test them side-by-side in batches, or whatever you feel is best. You should spend the most time in this step. Once you’re confident in the prompt, you can move on to the last step.

Diff checker view of 2 system messages and prompts
In PromptHub, you can easily test different prompt versions and track granular differences

Step 4: Test with Different Data

Now that the model, parameters, and prompt are all set, let’s make sure this prompt works well with different data injected, rather than with just the base data we’ve been using. You can do this via datasets in PromptHub. Upload a CSV or create a quick dataset by hand, and then run your prompt over that dataset.

A text box in PromptHub with variables and text
Test a prompt and dynamically inject data from a CSV

Step 5: Get Your Team Involved to Review!

After each step, you should commit your changes or open up a merge request so that your team can review what you’ve done, test it further, and give you notes. Prompt engineering is better done with various perspectives, and you get better outputs by getting fresh eyes on the problem. PromptHub has a variety of prompt versioning features to help make this easy.

A merge request in PromptHub web application
Open up merge requests in PromptHub to get your team to review changes

Wrapping up

We've covered a lot of ground here. We looked at some foundational principles, prompt patterns, and advanced methods you can leverage to get better outputs when writing prompts to generate content. By understanding and applying these techniques, you can get content that is actually of high quality and relevance.

We say it all the time, but prompt engineering is an iterative process. It takes some work. Utilize the insights from the experts quoted here and get to testing. Whether you're crafting LinkedIn posts or detailed reports, these strategies will help you when writing content generation prompts.

Dan Cleary
Founder and CEO