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Duration 7 hours
Course Outline
Transitioning from Text Generation to Typed Decisions
- Understanding the output structure of decision models.
- Distinguishing System One decisions from reasoning LLMs.
- Analyzing the cost and latency profiles of bounded decisions.
The Ecosystem of Open-Weight Decision Models
- Strands Decider 2B from AWS Labs.
- The JEV and GEV open-weight model families.
- Overview of Pplx-Decider, Liquid D1, and Clef.
- Comparing licensing and deployment considerations.
Architectural Insights into Decision Models
- Utilizing an LLM torso without a language modelling head.
- Scoring candidate options using a pointer head.
- Deriving confidence scores.
Executing Initial Decisions
- Local configuration on CPU and consumer-grade GPUs.
- Formulating questions and defining option sets.
- Processing multiple queries in a single pass.
- Managing thresholds and fallback mechanisms.
Evaluating Decision Quality
- Reviewing JevBench and other decision benchmarks.
- Balancing accuracy against calibration.
- Interpreting Brier scores and confidence reliability.
- Recognizing scenarios where decision models are unsuitable.
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 workflows.
- Software developers seeking to substitute LLM calls with bounded decisions.
- Solution and platform architects designing robust agent architectures.
- Technical product managers evaluating decision model capabilities.
