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Duration 7 hours
Course Outline
From Text Generation to Typed Decisions
- Understanding the output structure of decision models
- Contrasting System One decisions with reasoning LLMs
- Cost and latency characteristics of bounded decisions
The Open-Weight Decision Model Landscape
- Introducing Strands Decider 2B from AWS Labs
- Overview of the JEV and GEV open-weight families
- Examination of Pplx-Decider, Liquid D1, and Clef
- Differences in licensing and deployment strategies
Anatomy of a Decision Model
- Utilizing an LLM torso without a language modelling head
- Scoring candidate options via the pointer head
- Origins of the confidence score mechanism
Implementing Your First Decisions
- Local setup on CPUs and consumer-grade GPUs
- Formulating effective questions and option sets
- Processing multiple queries in a single pass
- Configuring thresholds and fallback behaviors
Assessing Decision Quality
- Exploring JevBench and other decision benchmarks
- Balancing accuracy with calibration
- Evaluating Brier score and confidence reliability
- Recognizing tasks where decision models are ill-suited
Requirements
- Fundamental understanding of large language models and prompt-based applications.
- Proficiency with command-line interfaces and Python scripting.
- No prior experience in machine learning training is necessary.
Target Audience
- AI engineers assessing decision models for agent-based workflows
- Software developers seeking to replace broad LLM calls with bounded decisions
- Solution and platform architects designing agent architectures
- Technical product managers evaluating decision model capabilities
