lead to generic responses. Blind Trust in the Model: Over-reliance on the model's capabilities without verification. No Examples: not providing example inputs and outputs. Misplaced Belief in Model's Understanding: Assuming the model can intuitively understand your meaning. Ignoring Obsolescence: Failing to update prompts in line with model updates or changes in data.
intelligence analyst, analyze the provided data to identify MITRE ATT&CK techniques, and present in Markdown with columns for ID, Description, and Comments. Here are my data: <data> Role Definition / Contextual Awareness Clear Objectives Iterative refinement
for task understanding. Zero-Shot vs Few-Shot Zero-Shot: No example prompts. Few-Shot: Uses example prompts for clarity. Why Use Few-Shot Prompting? Enhances task-specific accuracy. Ideal for complex or nuanced tasks.
models Two Phases: Retrieval & Generation Retrieval: Searches Database Generation: Context-Relevant Response Customize with Your Own Data! Prepare your data Tokenization Split in smaller Chunks Embeddings and Vector Model is ready for input
Allows agents to perform complex tasks through a series of actions. Core Components Reason: The agent's thought process to decide the next action. Act: The actual action taken by the agent based on reasoning. How Does It Work? Action → Observation → Thought Cycle The agent performs an action. Observes the result. Thinks (Reasons) about the next step. https://peterroelants.github.io/posts/react-repl-agent/
threat intelligence. Crafting the right prompt The importance of clearly defining objectives in prompt engineering. Mastering Techniques Understanding various methods like Few-Shot Learning, RAG, ReAct, etc.