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Duration 7 hours
Course Outline
Transitioning from Text Generation to Typed Decisions
- Understanding the output of a decision model
- Contrasting System one decisions with reasoning LLMs
- Analysing the cost and latency profiles of bounded decisions
The Open-Weight Decision Model Landscape
- AWS Labs’ Strands Decider 2B
- The JEV and GEV open-weight families
- Overview of Pplx-Decider, Liquid D1, and Clef
- Differences in licensing and deployment
Inside a Decision Model
- The LLM torso structure without a language modelling head
- How the pointer head scores candidate options
- Origin and derivation of confidence scores
Executing Your First Decisions
- Local setup on CPU and consumer-grade GPUs
- Structuring questions and defining option sets
- Processing multiple questions in a single pass
- Managing thresholds and fallback behaviours
Evaluating Decision Quality
- JevBench and other decision-focused benchmarks
- Balancing accuracy against calibration
- Interpreting Brier scores and confidence reliability
- Identifying tasks unsuited for decision models
Requirements
- Fundamental knowledge 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 evaluating decision models for agent workflows
- Software developers looking to substitute LLM calls with bounded decisions
- Solution and platform architects designing agent-centric architectures
- Technical product managers assessing decision model capabilities
