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Duration 7 hours
Course Outline
Transitioning from Text Generation to Typed Decisions
- Understanding the output of decision models.
- Comparing System One decisions with reasoning LLMs.
- Examining the cost and latency profile of bounded decisions.
The Open-Weight Decision Model Ecosystem
- AWS Labs’ Strands Decider 2B.
- JEV and GEV open-weight model families.
- Other models including Pplx-Decider, Liquid D1, and Clef.
- Differences in licensing and deployment strategies.
Internal Architecture of Decision Models
- Utilizing an LLM torso without a standard language modelling head.
- Scoring candidate options via the pointer head.
- Understanding the derivation of confidence scores.
Executing Initial Decisions
- Local setup on CPU and consumer-grade GPUs.
- Formulating questions and defining option sets.
- Processing multiple questions in a single pass.
- Configuring thresholds and fallback behaviors.
Evaluating Decision Quality
- Reviewing JevBench and other decision benchmarks.
- Balancing accuracy against calibration.
- Analyzing Brier scores and confidence reliability.
- Recognizing scenarios where a decision model is not the optimal tool.
Requirements
- Basic understanding of large language models and prompt-based applications.
- Proficiency with command-line interfaces 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 decision logic.
- Solution and platform architects designing agent-based architectures.
- Technical product managers assessing decision model capabilities.
