Prompt engineering for production AI systems requires a structured approach beyond simple demonstrations, focusing on reliability and task-specific needs. Engineers must select appropriate prompting patterns, such as Zero-shot, Few-shot, Chain-of-Thought, or ReAct, based on the complexity of the task, reasoning requirements, and the need for external data or tools. The core principle involves combining a chosen pattern with curated context—including instructions, examples, schemas, or retrieved documents—to form a prompt that, after validation, feeds into the application. AI
IMPACT Provides engineers with a framework to select and apply the most effective prompting strategies for reliable production AI systems.
RANK_REASON The cluster discusses prompt engineering patterns and strategies for production AI systems, offering guidance and frameworks rather than announcing a new model or research breakthrough.
- Few-shot learning
- JSON
- Prompt Chaining for Complex Logic
- React
- retrieval-augmented generation
- Schema-driven construction of future autobiographical traumatic events: The future is much more troubling than the past
- self-critique
- SQL
- Tree of Thoughts: Deliberate Problem Solving with Large Language Models
- zero-shot learning
- Anthropic
- Azure
- Claude 3
- Gemini
- GPT-4
- LangChain
- LlamaIndex
- Microsoft
- OpenAI
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