AIIntermediate#Interaction#Alpha
Prompt Engineering
P
Definition
The art of crafting specific text inputs to get more accurate or specialized behavior from an AI agent.
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Concept Diagram
Key Takeaways
1
Prompt engineering is the art of crafting inputs to AI models to get optimal trading analysis outputs
2
Well-structured prompts include market context, constraints, and specific output formats
3
System prompts define the AI agent's persona, rules, and behavioral boundaries
4
Quality of output is directly proportional to quality of input — garbage in, garbage out
Practical Example
A poor prompt: 'Should I buy BTC?' A well-engineered prompt: 'Given BTC at $60K, DXY rising, and funding rates at -0.01%, analyze the risk/reward of opening a 10% portfolio long position with a 5% stop loss. Format as: signal, conviction, entry, stop, target.'
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Related Terms
AI
LLM ReasoningThe process by which an AI agent uses large language models (like DeepSeek or Claude) to parse news and market data into trading decisions.
AI
Chain of Thought (CoT)A prompting technique where the AI agent is encouraged to 'think step-by-step', improving logical reasoning in complex trading scenarios.
AI
Context WindowThe maximum amount of information (tokens) an AI model can 'remember' and process at any single moment during reasoning.
AI
RAG (Retrieval-Augmented Generation)An AI framework that allows LLMs to pull real-time data from external sources (blockchains/news) before generating a response.
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