Mastering Prompt Engineering

Professional AI engineering is much more than "Asking a question." It is about using Logical Frameworks to force the AI to think accurately and predictably.

1. Few-Shot Prompting

Don't just give an instruction; give Examples. By providing 3-5 examples of the desired input/output format, you significantly improve the model's ability to follow complex schemas.

2. Chain-of-Thought (CoT)

Ask the model to "Think step-by-step." This forces the LLM to allocate more compute time to reasoning before giving a final answer. This is mandatory for math, logic, or complex code refactoring.

3. ReAct (Reason + Act)

This is the foundation of **AI Agents**. The model is told to write down its Thought, then perform an Action (like searching the web), then record an Observation, then repeat. This loop allows the AI to solve problems it doesn't know the answer to initially.

4. Interview Mastery

Q: "What is 'Hallucination' and how do you prevent it?"

Architect Answer: "Hallucination happens when the model predicts the next word based on probability instead of facts. We prevent this by: 1) **Grounding**: Providing the facts in the prompt (RAG). 2) **Negative Constraints**: 'If you don't know the answer, say target NOT FOUND.' 3) **Verification**: Asking a second AI model to review the first model's output for inaccuracies."