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Duration 7 hours
Course Outline
From Text Generation to Typed Decisions
- What a decision model returns
- System one decisions versus reasoning LLMs
- Cost and latency profile of bounded decisions
The Open-Weight Decision Model Landscape
- Strands Decider 2B from AWS Labs
- JEV and GEV open-weight families
- Pplx-Decider, Liquid D1, and Clef
- Licence and deployment differences
Inside a Decision Model
- LLM torso without a language modelling head
- Pointer head scoring over candidate options
- Origin of the confidence score
Running Your First Decisions
- Local setup on CPU and consumer GPU
- Phrasing questions and defining option sets
- Asking multiple questions in a single pass
- Managing thresholds and fallback behaviour
Evaluating Decision Quality
- JevBench and decision benchmarks
- Accuracy versus calibration
- Brier score and confidence reliability
- Tasks 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.
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 capabilities.
