Prompt Engineering

Master the discipline of instructing Large Language Models (LLMs). Prompt engineering is not just talking to AI; it is the programmatic configuration of input constraints to achieve deterministic outputs.

Module 1: The Evolution of AI

Artificial Intelligence (AI) encompasses systems engineered to perform tasks that historically required human cognition.

  +-----------------------------------------------------------------------+
  |                             EVOLUTION OF AI                           |
  |  1950s+: Rule-Based AI (If-Then Logic)                                |
  |     └─► 1980s+: Machine Learning (Statistical Pattern Recognition)    |
  |           └─► 2010s+: Deep Learning (Neural Networks)                 |
  |                 └─► 2020s+: Generative AI (Content Generation)        |
  |                       └─► 2023+: Agentic AI (Autonomy & Action)       |
  +-----------------------------------------------------------------------+

Module 2: The P.A.C.E. Framework

To avoid hallucinations and generic outputs, structure your prompts using Purpose, Action, Context, and Expectation.

Module 3: The Shot Spectrum

In-context learning allows you to program the AI without touching its backend code.

• Zero-Shot: Direct instruction only (Simple tasks)
• One-Shot: Task + 1 Example (Defines structure and tone)
• Few-Shot: Task + 4-5 Examples (Establishes complex pattern rules)

Module 4: Advanced RAG Systems

Retrieval-Augmented Generation (RAG) grounds your AI in your company’s proprietary data. Production RAG uses Hybrid Search (Dense Vector + Sparse BM25) and Cross-Encoder Reranking to achieve 95%+ precision.