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Duration 7 hours
Course Outline
From Text Generation to Typed Decisions
- The nature of decision model outputs.
- System one decisions compared with reasoning LLMs.
- Cost and latency characteristics of bounded decisions.
The Open-Weight Decision Model Landscape
- Strands Decider 2B from AWS Labs.
- The JEV and GEV open-weight families.
- Pplx-Decider, Liquid D1, and Clef.
- Differences in licensing and deployment.
Inside a Decision Model
- LLM torso structure without a language modelling head.
- Pointer head scoring across candidate options.
- Derivation of the confidence score.
Running Your First Decisions
- Local setup on CPU and consumer GPUs.
- Formulating questions and defining option sets.
- Processing multiple questions in a single pass.
- Managing thresholds and fallback behavior.
Judging Decision Quality
- JevBench and other decision benchmarks.
- Accuracy versus calibration.
- Brier score and confidence reliability.
- Scenarios where a decision model is inappropriate.
Requirements
- Basic understanding of large language models and prompt-based applications.
- Proficiency with the command line and Python scripting.
- No prior machine learning training experience is required.
Target Audience
- AI engineers evaluating decision models for agent workflows.
- Software developers seeking to replace LLM calls with bounded decisions.
- Solution and platform architects designing agent architectures.
- Technical product managers assessing decision model options.
