The Prompt LLM node is one of the most powerful tools in the Agent Builder. It allows you to turn raw inputs—research, scraped content, user data, or Profound insights—into clear, useful outputs such as summaries, outlines, rewrites, analyses, and structured responses.

This comprehensive guide teaches you how to write highly effective prompts without needing any background in prompt engineering.

Why good prompts matter

Large Language Models (LLMs) are excellent pattern recognizers, but they need direction. A well-written prompt helps the model understand:

• What task to perform

• How to perform it

• What style or format to use

• What constraints to follow

The clearer your instructions, the more accurate and repeatable your output will be. Think of prompts as creative briefs for the AI: the more context you give, the better the work.

The core structure of an effective prompt

Most great prompts follow this simple structure (but do not have to):

1. Role: Who or what should the model act as?

2. Task: What should it produce?

3. Context: What information should it use?

4. Requirements: Rules, guidelines, constraints

5. Output format: How the result should be structured

Below is an example prompt written with this structure:

You are an expert content strategist. Summarize the following research into 5 concise insights for a B2B marketing audience.

Context:
{{Web Page Scrape Output}}

Requirements:
- Focus only on key trends
- Avoid duplicate points
- Keep each insight under 20 words

Output Format:
- Bullet points only

This structure works across nearly every type of prompt you write in Profound.

The 5 elements of strong prompts

1. Give the model a role (optional but helpful)

LLMs perform better when told how to think.

Examples:

• “You are a senior SEO strategist who writes in a professional, concise tone.”

• “Act as a technical explainer for a non-technical audience.”

Assigning a role shapes the voice and decision-making of the model.

2. Be explicit about the task

Ambiguous instructions = unpredictable results.

Weak:

“Write something about this.”

Strong:

“Rewrite the following paragraph to be clearer and more concise, while keeping the core meaning.”

Even stronger:

“Rewrite the following text for a C-suite B2B audience. Reduce fluff. Improve clarity. Keep all data accurate.”

3. Provide context (the AI cannot guess)

The Prompt LLM node can reference any previous step with / variables.

Good context sources include:

• Web Page Scrape

• YouTube Transcript

• Research Snippets

• Perplexity Search results

• Answer Engine Insights

Include only relevant context—too much noise reduces quality.

Example:

Based only on this content:
{{YouTube Video Transcript Output}}

4. Add clear requirements and constraints

This is where you shape the output to your needs.

Common constraints marketers use:

• Tone: professional, friendly, authoritative, neutral

• Length: 3 bullets, 100 words, 1 paragraph

• Audience: B2B marketers, enterprise executives, small business owners

• Content rules: no jargon, avoid repetition, do not hallucinate facts

• Brand style: simple, direct, value-driven

Example:

Requirements:
- Keep introduction under 50 words
- Do not invent data or statistics
- Avoid phrases like “in today’s world”

5. Specify the format of the output

LLMs are much more accurate when you tell them exactly what shape the output should take.

Examples:

• Bullet list

• JSON

• Table

• One-paragraph summary

• Step-by-step instructions

• Title + 3 subtitles + CTA

Example prompt:

Return your answer in JSON with fields:
- insight
- supporting_quote
- source_url

Prompt patterns you can use right away

Below are reusable templates tailored for common marketing Agents.

1. Summarize content

Act as a senior content strategist. Summarize the key points from the text below for a B2B marketing audience.

Context:
{{Perplexity Search Output}}

Requirements:
- Provide 5 bullet points
- No more than 20 words per point
- Avoid repetition
- No fluff

Output format: Bullet list only.

2. Rewrite for clarity or tone

Rewrite the following text to be clearer, more direct, and suitable for an enterprise executive audience.

Context:
{{Web Page Scrape Output}}

Requirements:
- Remove jargon
- Keep factual accuracy
- Shorten sentences where possible

Output format: One clean rewritten paragraph.

3. Generate titles or headlines

Generate 10 high-performing article titles based on the topic below.

Topic: {{topic}}

Guidelines:
- Clear, crisp, compelling
- Avoid clickbait
- Include a strong, specific angle
- Keep under 60 characters when possible

Output format: Numbered list.

4. Extract structured data

Extract key insights from the content below.

Context:
{{YouTube Video Transcript Output}}

Requirements:
- Identify 3 themes
- For each theme, provide a 1-sentence explanation and a supporting quote (if available)

Output format (JSON):
[
  { "theme": "", "summary": "", "quote": "" }
]

5. Compare or evaluate content

Act as a content analyst. Compare the strengths and weaknesses of two competitor pages.

Context A:
{{Web Page Scrape Output A}}

Context B:
{{Web Page Scrape Output B}}

Requirements:
- Identify 3 strengths and 3 weaknesses for each page
- Keep explanations under 15 words

Output format: A table with columns: Page, Strength, Weakness.

Tips for getting consistently better outputs

✔ Tell the AI what to avoid

Hallucinations decrease significantly when you include rules like:

• “Do not add facts that are not in the context.”

• “If information is missing, say ‘Not enough information’.”

✔ Use examples

“Show, not tell” is extremely effective.

For example, a good headline looks like:
- “How AI Improves Inventory Forecasting”
- “The Future of Predictive Supply Chains”

✔ Keep prompts short—but not vague

Overly long prompts can confuse the model.
Overly short prompts give it no direction.

Aim for 3–8 sentences + one format block.

✔ Iterate and refine

Try one prompt, review the output, and refine it.
Prompt writing is like briefing a writer.

✔ Use variables to make prompts dynamic

This is especially helpful when building scalable Agents.

Example variable-driven prompt:

Summarize the following content for an audience of {{audience_segment}}.

Context:
{{Web Page Scrape Output}}

Common mistakes and how to avoid them

✖ Asking the AI to “write something good”

→ Too vague. Always specify what makes it good.

✖ Forgetting to restrict the source

→ AI will make things up unless you say “Use only the context provided.”

✖ No format

→ Results become inconsistent, hard to work with in Agents.

✖ Too many goals in one prompt

→ Split complex tasks into sequential Prompt LLM nodes for cleaner outputs.

Advanced prompt writing (optional)

For users who want more precision:

• Chain of Thought:
  “Explain your reasoning step-by-step, but only return the final answer.”

• Transform → Evaluate → Improve loops:
  Create a second Prompt LLM node to critique and refine the first output.

• Zero-shot vs. few-shot prompting:
  Providing “example outputs” dramatically improves accuracy.

Example: Full high-quality prompt template

You are an expert B2B content strategist. Rewrite the article below so it is clearer,
more scannable, and optimized for AI-answer performance.

Context:
{{Generate Article Output}}

Goals:
- Improve clarity and remove fluff
- Strengthen section headers
- Ensure each section answers a real user intent
- Maintain accuracy; do not add unsupported facts

Output Format:
Return:
1. A revised article
2. A bullet list of the key improvements you made

Style:
- Professional, concise, and trustworthy

Final thoughts

Prompt writing is a skill that compounds over time.
You do not need to be a “prompt engineer”—you simply need to:

• Be clear

• Be specific

• Provide context

• Give constraints

• Define the output format

With these techniques, you will consistently generate strong, predictable results with the Prompt LLM node—unlocking everything from content summaries to fully automated content production pipelines.

If you'd like, I can now create prompt templates by use case (SEO, briefs, rewrites, competitor analysis, snippets extraction, etc.).