AIDevWorks

Agenda
Segment 1 — Understanding How Prompts Are Parsed

• How LLMs interpret input: tokenization, attention, and context windows
• What happens inside GPT when you write a prompt
• Zero-Shot and Few-Shot Prompting — when and how to use each
• Writing clear, specific, and contextual instructions

Hands-On:
Try short prompts in OpenAI Playground or Claude.ai, observe how slight changes alter model reasoning.

Segment 2 — Prompt Patterns & Frameworks
Common patterns:

• 🎭 Role Prompting
• 🧩 Chain-of-Thought
• 🔁 ReAct (Reason + Act)
• 🌳 Tree-of-Thought
• 🧰 Hands-On:

Design prompts using different patterns. Compare outputs for quality, tone, and reasoning depth.

What You’ll Learn

🧩 Core Architecture Components

• Large Language Model (LLM) as reasoning engine
• Vector Database for semantic search & embeddings
• RAG (Retrieval-Augmented Generation) for grounding responses

⚙️ Data & Context Flow
Query → Embedding → Search → Context Injection → Response Generation

🧱 Design Layers
Ingestion Layer → Vectorization → Retrieval → Reasoning → Response

🔄 Integration Blueprints
APIs, Agents, and Microservices for LLM workflows

🔒 Security, Scalability, and Observability considerations