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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
- Where the confidence score comes from
Running Your First Decisions
- Local setup on CPU and consumer GPU
- Phrasing questions and option sets
- Asking several questions in one pass
- Thresholds and fallback behaviour
Judging Decision Quality
- JevBench and decision benchmarks
- Accuracy versus calibration
- Brier score and reliability of confidence
- Tasks where a decision model is the wrong tool
Requirements
- Familiarity with the fundamentals of large language models and prompt-based applications.
- Proficiency with command-line interfaces and Python scripting.
- No prior experience in machine learning training is required.
Target Audience
- AI engineers assessing decision models for agent-based workflows.
- Software developers looking to substitute LLM calls with bounded decisions.
- Solution and platform architects designing agent architectures.
- Technical product managers evaluating decision model capabilities.
