LLM Prompt Engineering Workshop
Bryan Munoz
AI Agent Developer
Large Language Models
Consultant
ChatGPT
Claude
Google Gemini
Techqueria
Project Overview:
Developed and delivered an engaging workshop on advanced prompt engineering techniques for large language models (LLMs), tailored to optimize workflows and enhance user outputs in job applications, strategy development, and data-driven decision-making.
Link to Event Page: https://lu.ma/sb3lp5zo
Key Deliverables:
Comprehensive Training Content:
Covered LLM fundamentals, including tokenization, neural networks, and context limitations.
Introduced cutting-edge prompting techniques such as Retrieval Augmented Generation (RAG), Chain-of-Thought Prompting, ReAct (Reasoning & Acting), and Reverse Engineering.
Presented the PC-TFT Framework for creating effective prompts by addressing Persona, Context, Task, Format, and Tone/Style.
Practical Applications:
Demonstrated job optimization workflows, including ATS optimization and tailored resume strategies.
Applied philosophical principles (Cogency, Coherence, Validity, Soundness) to refine prompting outputs.
Developed formula-based approaches for crafting compelling cover letters and strategic content.
Live Demonstrations:
Showcased step-by-step problem-solving with live LLM interactions.
Provided examples of real-world applications, such as market analysis and strategic decision-making.
Results & Impact:
Received 4.8/5 out of 27 Attendees
Empowered participants to effectively use LLMs for diverse tasks, including job searching, content creation, and strategic planning.
Improved participants' understanding of advanced prompting techniques and their application to professional workflows.
Skills & Tools Utilized:
Prompt Engineering
LLM Optimization Techniques
Instructional Design
Data-Driven Decision-Making
