Advanced Prompt Engineering Techniques

• Prompt Engineering Unleashed – Decode LLM mechanics from input to output; master System vs. User prompts, key components, and meta-prompting with real examples.

• AI Agents & Communication – Learn why prompt engineering matters, apply Theory of Mind, leverage context, and use pro techniques for effective interaction.

• Three Pillars of Agent Power – Build robust agents with high-quality prompts, rich tool integration, and fine-tuning strategies.

• Newsroom Simulation Project – Design an AI-powered newsroom: requirements, frameworks, architecture, and deliverables.

• Lab: Prompting Techniques – Create a Prompt Engineering glossary, experiment with self-critiquing agents, and build high-quality CrewAI/OpenAI-driven systems.

• Extract Structured Data from PDFs & Query It – Transform unstructured shipping guides into intelligent, queryable databases with AI-powered extraction and cleaning.

• Real-World Wow Factor – Build a FedEx Shipping Cost Chatbot from scratch as a standout portfolio project.

• Hands-On Adventure – Experience the full pipeline from AI prompting to SQL, turning raw data into actionable insights.

• Future-Ready Superpowers – Leverage Vanna AI and Qdrant to translate natural language into precise queries.

• Speedy Success – Complete an end-to-end project in a single session with expert guidance and confidence-building results.

• Tools & Agentic Systems – Explore the role of tools in agentic workflows, overcome data extraction challenges, master prompting as engineering, apply RAG for Text-to-SQL, understand the four pillars, and tackle the “Tower of Babel” integration issue.

• Model Context Protocol (MCP) – Learn MCP structure, core components, tool-resource boundaries, the importance of descriptions, fast server implementation, design principles, standardization for agent discovery, and lab specifications.

• ReAct Paper Summary – Understand interleaved reasoning-action methods, the observe-reason-act loop, “thought” as action, and evaluate real-world examples and outcomes.

• MemGPT Paper Summary – Examine infinite memory simulation, RAG-based architectures with memory management, core components, and approaches to human-like memory mimicry.

• Core Project: AI News Platform – Build a collaborative agent-based news analysis system to gather, fact-check, and transform raw data into refined articles on selected topics.

• Theory Foundations – Explore the evolution of messaging systems, communication models, agent discovery, OSI model for agents, and the CAP theorem in distributed agent networks.

• Architectures & Blueprints – Understand scalable messaging, the Internet of Agents, and key design principles for enterprise-grade infrastructures.

• Lab Focus – Build a Python + FastMCP backend with custom tools (e.g., YouTube transcriber, weather forecaster) and integrate with CrewAI and LangGraph for orchestration.

• Advanced Skills – Master GPU memory management, debugging, and optimization for high-performance AI agents.

• Why It Matters – Achieve hands-on, portfolio-ready expertise in designing robust, real-world agent systems.

• Agent Evolution & MCP – Trace agent history, core pillars, and MCP’s role in seamless integration and distributed execution.

• Scaling Architectures – Harness Kubernetes for containerized scaling and transition from legacy (MapReduce, Spark) to modern Ray ecosystems.

• Ray Mastery – Integrate MCP with Ray, reuse tools, optimize models, and transform APIs into agent-ready assets.

• Lab Project – Build a voice-powered multi-agent media system with CrewAI, Whisper, and Streamlit to generate newsletters, slides, and podcasts.

• Career Edge – Graduate with a portfolio-ready project showcasing enterprise-grade AI automation.

• Agent Foundations – Frameworks vs. custom builds, Agentic RAG, and smarter retrieval strategies.

• Learning & Scaling – Fine-tuning methods, neural scaling laws, transfer learning, and decision optimization.

• System Efficiency – GPU memory optimization, semantic caching, real-time serving, and query routing.

• Lab Project – Build scalable LangGraph agents with MCP & Ray: a multi-service app solving complex animal queries with RAG.

• Career Edge – Showcase enterprise-grade design, scaling, and fine-tuning in your portfolio.

• Core Skills – Dynamic query plans with expansion, guardrails, semantic caching, rerankers, and confidence checks.

• Relevance Optimization – Fine-tune relevance detectors (e.g., BERT) for high accuracy with GPU-aware batching and <5ms latency.

• Scaling & Efficiency – Apply parameter-efficient fine-tuning, quantization, and Ray-based scaling to push performance.

• Agent Design Patterns – ReAct loops, LangGraph workflows, multi-agent planning, and the Five Pillars (prompts, tools, orchestration, memory, fine-tuned models).

• Lab Project – Build & deploy Relevance-Detecting LangGraph Agents with MCP & Ray—portfolio-ready proof of scalable, intelligent agent design.

• Barriers to Fine-Tuning – Overcome hardware costs, VRAM scaling, and catastrophic forgetting.

• Representation Learning – Grasp the Universal Approximation Theorem and data complexity insights for smarter models.

• Selective Layer Tuning – Target key layers with data-driven strategies for efficient adaptation.

• PEFT Revolution – Cut costs with prompt tuning, soft prompts, and deep prompt tuning.

• Agentic LoRA – Apply LoRA’s low-rank projections and adapters to scale adaptive agents.

• Reinforcement Learning Edge – Explore RL fundamentals, exploration benefits, and the 2025 reasoning leap.

Lab Project: Build an Autonomous Newsletter Generator—from web research to validated, polished newsletters.

Tech Stack Mastery: LangGraph + Ollama + FastMCP with GPU optimization for enterprise-ready performance.

Career Edge: Create a portfolio-ready autonomous AI app that proves your mastery of fine-tuning, orchestration, and real-world deployment.

• Capstone Kickoff – Launch your final project, blending all bootcamp skills into one AI showcase.

• RL Foundations – Learn exploration vs. exploitation, states, actions, rewards, and long-term strategy.

• Learning Dynamics – Decode the agent loop: trajectories, episodes, and emergent reasoning.

• RL Frameworks – Master actor-critic models, PPO optimization, and training challenges.

• InstructGPT & RLHF – Dive into pre-training, SFT, and reinforcement with human feedback—plus ethics and limits.

Lab Project: Build a Semantic Cache for RAG—fine-tune sentence embedders and deploy scalable microservices.

Optimization Power: Integrate with FastMCP to slash costs, speed up responses, and maximize efficiency.

Career Edge: Add a portfolio-ready capstone proving mastery in RL, RAG optimization, and enterprise AI systems.