You need to write 50 personalized outreach emails. Each one should reference the recipient's company, their recent news, and why your solution fits.
Your team spends 20 minutes per email. That's 16+ hours of work.
Or you could tell an AI what makes a good outreach email and let it draft all 50 in minutes.
Text generation isn't about replacing writers. It's about scaling judgment.
CORE AI PRIMITIVE - This is the foundation of most AI automation. Every chatbot, content generator, and AI assistant depends on text generation.
Text generation is giving an AI model a prompt and getting back written text. You describe what you want: 'Write a follow-up email to a prospect who downloaded our whitepaper.' The model generates text that follows your instructions, matches your specified tone, and incorporates any context you provide.
Modern language models have learned patterns from vast amounts of text. They don't retrieve or copy. They generate new text word by word, predicting what should come next based on everything that came before. The result feels like it was written by someone who understood your instructions.
The magic isn't the generation itself. It's that you can encode your judgment into prompts. 'Sound professional but warm, mention their recent funding round, keep it under 150 words.' The AI applies that judgment at scale.
Text generation solves a universal problem: how do you apply human judgment to tasks that require language understanding, without requiring a human for every instance?
Encode your criteria and context into a prompt. Let the model generate output following those criteria. Review and refine the prompt based on output quality. This pattern scales from single generations to millions.
Adjust tone and temperature. Click "Generate Again" to see variation. Low temperature = consistent. High temperature = creative but unpredictable.
Click multiple times with high temperature to see variation.
Hi Jennifer! Great seeing you at the webinar yesterday! Your question about integration timelines was spot-on. Would love to chat more about how we typically get clients up and running in 2-3 weeks. Coffee next week? Cheers, Sarah
Low temperature: Click "Generate Again" multiple times. Notice the output is identical each time. Good for consistency, bad for creativity.
One prompt, one response
The simplest pattern. You send a complete prompt with all context and instructions. The model returns a complete response. Good for standalone tasks like drafting an email or summarizing a document.
Generate, evaluate, regenerate
Generate an initial output, evaluate it against criteria, and regenerate with feedback. 'That email was too formal, make it warmer.' This pattern lets you steer the output toward exactly what you need.
Constrain output to match a schema
Force the model to output valid JSON, XML, or other structured formats. Instead of 'write me some data,' you say 'return a JSON object with these exact fields.' The output is guaranteed to parse correctly.
Your marketing team needs to follow up with every webinar attendee. Each email should reference their company, what they asked during Q&A, and relevant case studies. This flow generates all 47 drafts in minutes, ready for human review.
Hover over any component to see what it does and why it's neededTap any component to see what it does and why it's needed
Animated lines show direct connections · Hover for detailsTap for details · Click to learn more
You write a prompt, get a decent result, and ship it. Three weeks later, edge cases are failing everywhere. The prompt that worked for your test cases falls apart when it sees real-world variety.
Instead: Treat prompts as code. Version them. Test them against diverse examples. Iterate based on failures.
You leave temperature at default (often 0.7-1.0) for everything. Your customer support responses have wild variation. Some are perfect, some are weirdly creative. Users notice the inconsistency.
Instead: Lower temperature (0.1-0.3) for factual, consistent tasks. Higher (0.7-1.0) only when you want creativity.
You trust the model output and pass it directly to users or downstream systems. Then the model hallucinates a policy that doesn't exist, makes up a discount code, or outputs invalid JSON that crashes your pipeline.
Instead: Always validate. Check facts against source data. Parse structured output. Have fallbacks for failures.
You've learned how prompts become text. The natural next step is understanding how to reliably extract structured data from that text.