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Writing Effective Prompts for AI Enrichment

Last updated on Apr 14, 2026

Writing Effective Prompts for AI Enrichment

A great prompt is the difference between mediocre AI results and results that genuinely move the needle for your business. The good news? Learning to write prompts that work is surprisingly straightforward once you understand a few key principles.

Think of your prompt as a conversation with a very smart colleague who's willing to help but needs clear instructions. The more specific and thoughtful you are, the better your colleague (the AI) can help you.

Prompt Writing Best Practices

Before we get into specific examples, here are the fundamentals of effective prompt writing:

1. Be Specific

Vague prompts produce vague results. Always tell AI exactly what you want.

Weak prompt: "Write about the company."

Strong prompt: "Write a 2-sentence summary of {{company}}'s main products and target market. Focus on what makes them unique. Keep it under 50 words."

The strong version tells AI the length, focus, and format. No guessing.

2. Provide Context

The more background information you give, the better results you'll get. If you're writing a message, tell AI who it's for and what you want to achieve.

Weak prompt: "Write an email."

Strong prompt: "Write a friendly, professional email to {{firstName}}, who works in {{department}} at {{company}}. We're reaching out to offer {{productName}}. The email should address a pain point specific to their industry and include a clear call to action. Keep it to 3-4 paragraphs."

Context helps AI understand the situation and adjust its tone and content accordingly.

3. Use Column Variables Liberally

Column variables like {{firstName}}, {{company}}, {{industry}} make your enrichment personal and dynamic. They're the secret sauce that transforms generic outputs into relevant, targeted results.

Without variables: "Write a message."

With variables: "Write a personalized message to {{firstName}} at {{company}}. They work in {{industry}} and their company size is {{employeeCount}}. Mention something relevant to {{industry}} businesses."

Variables turn a one-size-fits-all prompt into 1,000 personalized enrichments.

4. Set Clear Output Format Expectations

Tell AI exactly what format you want. Numbers, JSON, bullet points, paragraphs. Be explicit.

Unclear: "Tell me if this lead is good."

Clear: "Rate this lead from 1-10. Answer with only the number. Use this scale: 1-3 (not qualified), 4-7 (might work), 8-10 (very qualified)."

Clear format expectations = cleaner, more usable results.

5. Use System Prompts vs. User Prompts

In TexAu, you're writing a "user prompt," the specific request for each row. Think of system prompts as standing instructions that would apply to all requests (e.g., "You are a sales expert. Be concise. Use simple language.").

In TexAu, we handle system instructions for you. Your user prompt should focus on the specific task at hand.

6. Keep It Conversational

You don't need to use fancy language or technical jargon. Write like you're talking to a smart colleague. AI understands natural, conversational English perfectly well, and often prefers it.

Stiff: "Enumerate three salient characteristics of the aforementioned entity's operational paradigm."

Natural: "List three key things about how {{company}} operates."

Simple, conversational prompts often produce better results.

Good vs. Bad Prompt Examples

Let's look at real side-by-side comparisons to see how these principles play out:

Example 1: Lead Scoring

Bad Prompt:

Score this lead.

Why It's Bad: No context, no scoring criteria, no format, no guidance on what makes a good lead.

Good Prompt:

Score this lead on a scale of 1-10 based on fit for our {{productName}} solution.
Company: {{company}}
Industry: {{industry}}
Employee Count: {{employeeCount}}
Use this scale:
- 1-3: Poor fit (wrong industry, too small, not a buyer)
- 4-6: Possible fit (could use our solution)
- 7-10: Excellent fit (high-priority prospect)
Answer with only the number.

Why It's Better: Specifies criteria, provides context, sets format, gives examples of scoring tiers.

Example 2: Personalized Messaging

Bad Prompt:

Write an outreach message.

Why It's Bad: Who is it for? What's the goal? What's the tone? Way too open-ended.

Good Prompt:

Write a friendly 2-sentence LinkedIn connection request for {{firstName}}, who works as a {{jobTitle}} at {{company}}.
Research shows {{company}} is in the {{industry}} space.
Mention something specific about their company or industry to show you've done your homework.
Keep it warm and genuine. No sales pitch.

Why It's Better: Names the recipient, specifies length, asks for a specific detail, sets the tone clearly.

Example 3: Company Research

Bad Prompt:

Tell me about {{company}}.

Why It's Bad: Vague output, no length guidance, no focus on what matters to your business.

Good Prompt:

Write a 3-4 sentence summary of {{company}} for a sales rep preparing for an outreach call.
Focus on:
1. What {{company}} does and their main products
2. Who their likely target customers are
3. Any recent news or trends in their industry

Keep it concise and actionable. Avoid buzzwords.

Why It's Better: Specific length, focus areas, intended audience, and tone all clarified.

Common Prompt Patterns You Can Use

Here are battle-tested prompt patterns from our most successful customers. Copy, customize, and use them as a starting point:

Pattern 1: Lead Scoring

Score {{firstName}} from {{company}} on fit for our solution (scale 1-10).
Context:
- Their industry: {{industry}}
- Company size: {{employeeCount}} employees
- Their role: {{jobTitle}}
- Budget indicator: {{budgetLevel}}

Scoring scale:
- 1-3: Not a fit
- 4-6: Could be a fit
- 7-10: Strong fit for our solution

Answer with only the number (1-10).

Pattern 2: Personalized Email Open

Write a 2-3 sentence email opening for {{firstName}} at {{company}}.
Their role: {{jobTitle}}
Their industry: {{industry}}
Key detail: {{companyFocus}}

Make it personalized and specific to their situation. Avoid generic language. Show that you've done your research.
Start with the opening line only. Don't include greeting or sign-off.

Pattern 3: Company Summary for Sales

Summarize {{company}} for a sales rep in 3 sentences.
What they do:
- Primary product/service: {{productType}}
- Target market: {{targetMarket}}
- Company size: {{employeeCount}}

Focus on: What they do, who they sell to, and what makes them stand out in their market.
Be concise and practical.

Pattern 4: Lead Classification

Classify {{firstName}} from {{company}} into ONE of these categories:
- Hot Lead: Actively buying, budget available, urgent
- Warm Lead: Interested, in research phase
- Cold Lead: Potential fit, early awareness stage

Context:
- Their role: {{jobTitle}}
- Industry: {{industry}}
- Recent activity: {{lastInteraction}}

Answer with only the category name.

Pattern 5: Sentiment Analysis

Analyze the sentiment of this customer feedback:
"{{feedback}}"

Rate sentiment as one of: Positive, Neutral, Negative

Then provide a 1-sentence summary of the main point.

Format your response as:
Sentiment: [One word]
Summary: [One sentence]

Pattern 6: Data Extraction & Cleaning

Extract the key information from this text:
"{{rawText}}"

Return as comma-separated values:
[Company Name], [Industry], [Headcount Range], [Contact Person Name]

If any information is not clearly stated or cannot be determined, write "Unknown".

Pattern 7: Insight Generation

Based on {{company}}'s profile, identify ONE key opportunity for our solution:
- Company: {{company}}
- Industry: {{industry}}
- Size: {{employeeCount}} employees
- Current tools: {{currentTools}}

What's their biggest pain point that our product solves?
Be specific and business-focused. One sentence only.

Testing & Iterating on Your Prompts

Great prompts aren't born perfect. They're refined through testing. Here's our process:

Step 1: Write Your First Draft

Don't overthink it. Write your initial prompt with the best practices in mind.

Step 2: Test on Sample Rows

Create your AI column and test it on 5-10 representative rows. Run the enrichment and review the results.

Ask yourself:

  • Are the outputs what I expected?

  • Is the tone right?

  • Is the format clean and usable?

  • Did the AI understand what I wanted?

Step 3: Spot Check & Analyze

Look at 3-5 outputs closely. Are they good? Are there patterns in what works and what doesn't?

Step 4: Refine Your Prompt

Based on what you see, adjust your prompt. Maybe:

  • Be more specific about tone or format

  • Add an example of what good output looks like

  • Simplify the instruction (you asked for too much)

  • Add context the AI might have missed

  • Clarify scoring criteria or definitions

Step 5: Test Again

Run it on your sample rows again. Does it improve?

Step 6: Scale It Up

Once you're happy, run it on your full dataset or set it up to run on new rows automatically.

Testing tip: Save your test results and notes. Over time, you'll develop intuition for what works. You might even build a library of prompts that work amazingly well for your specific business.

Prompt Templates You Can Customize

We've built a collection of ready-to-use prompt templates in TexAu. When creating a new AI column, check out our Template Library for prompts designed for common tasks. You can copy them and customize with your own column names and business details.

Look for templates in these categories:

  • Sales & Outreach

  • Lead Scoring & Qualification

  • Company Research

  • Data Classification

  • Sentiment & Feedback Analysis

  • Content Generation

  • Insight & Opportunity Identification

Common Mistakes to Avoid

Mistake 1: Asking for Too Much in One Prompt

Avoid: "Write an email, score the lead, and summarize the company."

Instead: Create separate AI columns for each task. It's cleaner and often produces better results.

Mistake 2: Forgetting to Mention Column Names in Examples

If you reference {{jobTitle}} in your prompt, make sure you actually have that column in your table. Typos or non-existent columns break things.

Mistake 3: Being Too Vague About Tone

AI can sound robotic, overly casual, or off-brand if you don't set clear tone expectations. Always specify: "professional but friendly," "formal and technical," "casual and conversational," etc.

Mistake 4: Ignoring Output Format

If you want a number or JSON or bullet points, say so explicitly. Don't assume AI will guess.

Mistake 5: Not Testing Before Running at Scale

Test on 5 rows before running 5,000 rows. A small prompt tweak saves thousands of credits.

Pro Tips from Our Best Users

Here's what the customers who get the best results from AI enrichment do differently:

Tip 1: They write prompts for their use cases, not generic ones. Specificity to your business > generic prompts.

Tip 2: They iterate constantly. They're not afraid to tweak and refine. Each iteration gets better.

Tip 3: They use variables liberally. The more personalization, the better the engagement.

Tip 4: They start simple. They write clear, concise prompts before trying complex ones.

Tip 5: They document what works. They keep notes on which prompts produce which results so they can reuse and build on them.

What's Next?

Ready to write your first prompt? Head to your TexAu workspace and create an AI column. Use the patterns and examples above as your starting point.

Need help with a specific use case? Check out our Using AI Enrichment guide for detailed walkthroughs of popular AI enrichment scenarios.

Happy prompting!