How to use Jev AI for fast decision making

TL;DR

Jev is a new AI approach that uses a non traditional model to categorize and route data for faster, cheaper decisions. It acts as a big if else framework that returns structured results with confidence scores, enabling code level automation without relying solely on conventional LLMs. The system aims for significant cost savings and speed while maintaining actionable outputs.

Transcript

What you're about to see in this video is the craziest thing I have come across in the AI in a long long time. And this is not a breakthrough in LLMs or you know AGI or any of that sorts but a very interesting application of using AI classifier with the learnings and with the performance improvements and benefits of LLMs that have been done in the ... Read More

Key Insights

• Jev is described as a big if else style AI that operates inside code to drive decisions
• The model focuses on frontier composible intelligence optimized for decisions rather than general text generation
• Costs are highlighted as 42 dollars per billion input tokens and output tokens are free, making it cheaper than some competitors
• Open source or alternative models such as GLM or OpenAI are contrasted with Jev in the talk
• The demonstration shows JSON shaped outputs rather than plain text, enabling deterministic integration
• Jev uses a questionnaire style to categorize input into departments with confidence levels
• The system can mark urgency and frustration as separate features with calibrated scores
• The video frames Jev as a tool to complement traditional LLMs rather than replace them

Questions & Answers

Q: What is Jev and how does it differ from traditional LLMs?

Jev is described as a big if else statement engine that can be embedded in code to make decisions based on input data. Unlike traditional LLMs that generate continuous text, Jev returns structured outputs such as JSON, category choices, and confidence scores, enabling deterministic behavior and faster, cheaper processing in workflows.

Q: How is Jev priced compared to other AI models?

The video states that Jev costs 42 dollars per billion input tokens, with output tokens being free. This pricing highlights a substantial cost advantage over typical large language model usage, where both input and output tokens usually incur costs, making Jev an economical option for decision oriented tasks.

Q: What practical example is used to illustrate Jev in action?

A practical example shows Jev classifying a user message into departments like technical, sales, or billing with confidence percentages. It demonstrates urgency and frustration scoring, and how a company could route queries automatically based on structured outputs rather than free form text.

Q: Can Jev work alongside other AI models?

Yes, the video emphasizes that Jev is not a blanket replacement for LLMs like Anthropic or OpenAI. It suggests using Jev for fast decision routing and then leveraging traditional models where richer language understanding is needed, effectively combining speed with capability in a layered approach.

Q: What is meant by frontier composible intelligence in relation to Jev?

Frontier composible intelligence refers to a modular, scalable AI approach where intelligence components can be composed and recombined for specific tasks. Jev is positioned as optimized for decisions, enabling rapid, deterministic workflows that can be integrated into software without relying on monolithic text generation.

Q: How does Jev handle non English or mixed language input in the demo?

In the transcript, Jev is shown handling mixed language inputs and still producing department classification with reasonable confidence levels. This demonstrates robustness to varied language patterns, suggesting that Jev can process real world, multilingual data to support decision routing.

Q: What is the role of confidence scores in Jev outputs?

Confidence scores accompany department classifications and other decisions, providing a measure of certainty. This allows downstream systems to decide whether to escalate, ask for clarification, or proceed, enabling safer automation and more nuanced workflow control.

Q: What is the broader goal of Jev according to the video creator?

The broader goal is to enable faster, cheaper AI assisted decisions by embedding a lightweight decision engine inside code. Jev aims to reduce reliance on heavyweight LLM calls for routine routing and categorization while still enabling integration with larger language models for tasks that require deeper understanding.

Summary & Key Takeaways

• Jev presents a non traditional AI workflow that replaces heavy LLM calls with a fast, structured decision engine

• The technology emphasizes cost efficiency, claiming prices as low as four cents per million input tokens and free output tokens

• The video demonstrates practical uses like routing customer queries to the correct department with confidence scores and fast execution