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Oak AI Campus
AI-Guided Mastery · Public Preview

Prompt engineering for product teams

Integrating LLM orchestration and prompt optimization into the product development lifecycle.

5 StepsINTERMEDIATE tierAdaptive ExamShareable Cert6 free credits to unlock
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Context and Role Specification

Establish high-fidelity system personas and few-shot exemplars.

System MessageFew-shot Prompting

Part 1/3 — Advanced Theory & Mechanics

The integration of Large Language Models (LLMs) into the product development lifecycle necessitates a transition from heuristic "chatting" to rigorous Context and Role Specification. At the core of this discipline is the systematic definition of the System Message—a privileged instruction set that establishes the model's operational boundaries, semantic persona, and epistemic constraints. Product teams must treat the prompt as a deterministic configuration layer rather than a natural language suggestion. By leveraging Role Specification, teams enforce a "latent space" orientation, steering the model toward a specific subset of its training data relevant to the product domain, whether that be clinical documentation, legal contract analysis, or technical support. This phase effectively minimizes the "hallucination surface area" by replacing broad general-purpose reasoning with specialized, bounded logic.

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Reasoning and Decomposition

Implement Chain-of-Thought (CoT) and Multi-step logic.

Chain-of-ThoughtTask Decomposition
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Structured Output and Validation

Ensure schema adherence for downstream system integration.

JSON ModeSchema Validation
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Iterative Evaluation Frameworks

Develop quantitative benchmarks for prompt performance.

Golden SetModel Evaluation
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Governance and Version Control

Manage prompt lifecycles through systematic versioning.

Prompt VersioningRegression Testing
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