Prompt Engineering for All

Although this event has already taken place, we’re excited to offer future sessions on the same topic! If you’re interested in attending our upcoming seminar with updated content, sign up below, and we’ll notify you when the next event is scheduled.

This event is designed to guide you through the full spectrum of prompt engineering techniques, from core strategies to expert-level methods. Whether you’re new to working with Large Language Models (LLMs) or looking to refine your skills, this seminar will equip you with practical techniques for every stage of expertise.

Core Prompting:

1. Role Playing
   Learn how to guide LLMs by assigning them specific roles, allowing for tailored and context-specific responses.
2. N-Shot Prompting
   Understand how to use a few examples (few-shot prompting) or no examples (zero-shot prompting) to direct LLMs toward desired outputs.
3. Role Reversal Prompting
   Explore techniques where prompts simulate reversed conversations, working backward from the outcome to lead the model.
4. Context-Based Prompting
   Master the art of providing contextual information to LLMs to generate accurate and relevant responses based on the prompt’s surroundings.

Advanced Prompting:

1. Reverse Prompting
   Discover how to craft reverse prompts to achieve more focused outputs by guiding LLMs from a final goal to an initial question.
2. LLM-Assisted Prompting
   Learn how to utilize LLMs to assist in creating and refining your prompts for improved performance and output precision.
3. Decomposed Prompting
   Break down complex tasks into simpler, manageable prompts to guide LLMs step by step toward solving intricate problems.
4. Prompt Chaining
   Build a sequence of prompts to guide LLMs through multi-step processes, ensuring each step follows logically from the last.
5. Hierarchical Prompting
   Understand how to organize prompts in a layered structure, leading LLMs through a hierarchy of tasks and subtasks.
6. Chain-of-Thought Prompting
   Explore techniques where prompts are designed to simulate step-by-step reasoning or thought processes for more complex tasks.
7. Hallucination-Mitigation Prompting
   Learn methods to minimize or prevent LLMs from generating incorrect or misleading information (hallucinations) during responses.

Expert Prompting:

1. Tree-of-Thought Prompting
   Develop prompts that guide LLMs through branching decision trees, allowing for more dynamic and adaptive reasoning paths.
2. Chain-of-Verification Prompting
   Create prompts that ensure the model verifies its own outputs, leading to more accurate and reliable responses.
3. Ensemble Prompting
   Combine multiple prompts and LLM outputs into a single coherent response, leveraging the strengths of various prompts.
4. Debiasing Prompting
   Learn advanced techniques to reduce bias in LLM responses, ensuring more fair and balanced outputs.
5. Prompt-Attack-Defense Prompting
   Explore defensive prompting strategies to safeguard LLM outputs from adversarial inputs or prompt-based attacks.

Interested in attending a future session?

Register for this event, and we’ll notify you when we schedule the next event!

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