mirror of
https://github.com/dair-ai/Prompt-Engineering-Guide
synced 2024-11-10 01:13:36 +00:00
37 lines
1.6 KiB
Plaintext
37 lines
1.6 KiB
Plaintext
# Prompt Engineering Course
|
|
|
|
import { Callout } from 'nextra/components'
|
|
|
|
Due to a high demand, we've partnered with Maven to deliver a [live cohort-based course on Prompt Engineering for LLMs](https://maven.com/dair-ai/prompt-engineering-llms). It's designed to learn more about real-world use cases and applications of prompt engineering and LLMs.
|
|
|
|
<Callout type= "info" emoji="🎓">
|
|
We are now offering a special discount for our learners. Use promo code MAVENAI20 for a 20% discount.
|
|
</Callout>
|
|
|
|
[Elvis Saravia](https://www.linkedin.com/in/omarsar/), who has worked at companies like Meta AI and Elastic, and has years of experience in AI and LLMs, will be the instructor for this course.
|
|
|
|
This technical, hands-on course will cover advanced prompt engineering techniques/tools, use cases, exercises, and projects for effectively working and building with large language models (LLMs).
|
|
|
|
Topics we provide training on:
|
|
|
|
- Taxonomy of Prompting Techniques
|
|
- Tactics to Improve Reliability
|
|
- Structuring LLM Outputs
|
|
- Zero-shot Prompting
|
|
- Few-shot In-Context Learning
|
|
- Chain of Thought Prompting
|
|
- Self-Reflection & Self-Consistency
|
|
- ReAcT
|
|
- Retrieval Augmented Generation
|
|
- Fine-Tuning & RLHF
|
|
- Function Calling
|
|
- AI Safety & Moderation
|
|
- LLM-Powered Agents
|
|
- LLM Evaluation
|
|
- Adversarial Prompting (Jailbreaking and Prompt Injections)
|
|
- Judge LLMs
|
|
- Common Real-World Use Cases of LLMs
|
|
|
|
Our past learners range from software engineers to AI researchers and practitioners in organizations like Microsoft, Google, Apple, Airbnb, LinkedIn, Amazon, JPMorgan Chase & Co., Asana, Intuit, Fidelity Investments, Coinbase, Guru, and many others.
|
|
|