How to use Jev prompts for faster AI builds

TL;DR

Jev can accelerate workflows by acting as a decision layer and prompt-driven router that speeds up tasks while cutting costs. Use it for categorization, triage, ad analysis, clipping, and more by wiring Jev to your data and tools, then calibrate with validation sets to improve accuracy.

Transcript

Jev is the new AI model that everyone's building with right now and for good reason because it can be up to 200 times faster and 400 times cheaper than other models. And so if you know exactly where to use it and how to use it, then it can definitely make your setup cheaper, make your systems faster, and ultimately transform your workflows. So toda... Read More

Key Insights

• Jev is a fast decision model that can pick from predefined options and never invent new categories.
• Triage workflows can use Jev to tag inquiries by category and confidence, routing low confidence items to humans.
• Competitor ad analysis can be automated by tagging ads with format, call to action, and funnel stage landing in a database.
• Clip finding with Jev can score and identify top moments from long videos in seconds.
• Customer base scoring with Jev groups customers into healthy or at risk segments for proactive outreach.
• Backlinking or internal linking can be built by Jev across large sites or second brain systems in seconds.
• Jev can help find buyers in comments by tagging comments as ready to buy, questions, or complaints.
• Validation sets allow measuring Jev accuracy without showing it the answers first to calibrate performance.

Questions & Answers

Q: A real search query question about the video (e.g. 'How to...', 'What is...', 'Why does...'). Question 1

How can Jev speed up handling customer inquiries and reduce costs while maintaining accuracy? Jev acts as the decision layer that tags inquiries into categories such as billing, bug, or question and also outputs a confidence score. By routing low confidence items to humans and high confidence ones to automation, you can scale support while keeping costs very low, as shown by a test where 217 transcripts were sorted in 13 seconds for 7 cents.

Q: What is a practical first use case to try with Jev according to the video?

A practical first use case is putting Jev in Google Sheets to categorize data. Since Jev can only select from your provided list, it quickly fills in a new column like category across many rows. This demonstrates speed and reliability of simple decision tasks without generating new text, and it helps you gauge speed versus accuracy before expanding.

Q: How does Jev assist with analyzing competitor ads?

Jev can read ad text quickly and tag each ad for format, call to action, and funnel stage, with the results landing in a database. This creates a swipe file of what is working in the market. In an example, Jev tagged 54 ads from three brands in about a second and a half for less than a cent, illustrating cost and time savings.

Q: What is the clip finder use case and how effective is it?

The clip finder uses Jev to score and select the best moments from long videos or podcasts. In the example, it scored 411 potential clips from a 70 minute interview in 6.5 seconds and highlighted the top 10 moments to post. This enables a single recording to yield ready to publish clips with minimal manual review.

Q: How can Jev help with customer lifecycle management in SaaS?

Jev can read customer data like tenure, plan, and cancellation risk to group customers into healthy, at risk, or churn candidates. This enables proactive outreach to those likely to cancel, helping to retain customers and optimize the health of the customer base without waiting for late signals.

Q: What is model routing and how does Jev optimize costs?

Model routing with Jev involves selecting the best model for a task and sending it to the appropriate one. For easy tasks, Jev directs to cheaper models, reserving larger models for harder jobs. This approach reduces token costs while maintaining performance by only using high cost models when necessary.

Q: How can Jev improve internal site navigation and linking?

Jev can build internal links across a site or a second brain system, helping users and agents navigate content more efficiently. In tests with 60 posts, linking occurred in about 2.5 seconds, improving page connectivity and searchability without relying on a large language model for every linking decision.

Q: What is the role of a validation set in Jev deployments?

A validation set consists of examples with known correct answers used to test Jev’s categorization accuracy. You run Jev on the set without revealing answers first, measure how often it matches, and identify misclassifications to calibrate future prompts. This helps ensure reliability before scaling Jev to production tasks.

Summary & Key Takeaways

• Jev speeds up data tasks by using decision style prompts to categorize, route, and optimize workflows within seconds.

• The video covers 19 concrete use cases from simple sheet categorizations to advanced model routing and extension builds.

• A free PDF guide is provided to copy these use cases into real projects and start building quickly.