Generative AI Essentials

LLM Generation Parameters

How to start learning Generative AI

🔅 Generative AI

Weekly Prompt

✨ Meeting Summary Scribe

🛠 Improving Prompts Effectiveness:

Today's prompt is:

Meeting Scribe

This prompt is aimed at

This prompt is aimed at professionals, team leaders, and administrative staff who are responsible for documenting meetings and communicating key information to stakeholders. The target audience includes anyone needing to summarize discussions effectively for clarity and accountability.

Those who will benefit from this include.

Individuals tasked with creating meeting minutes, team members who want to stay informed on action items, and managers looking to ensure follow-through on discussions will find this prompt beneficial. It helps improve communication and organization within teams.

📝 What This Prompt Does:

This prompt is designed to distill meeting notes into concise summaries that highlight discussion topics, key takeaways, and specific action items. By using this prompt, users can create clear and organized summaries that enhance understanding and facilitate accountability within teams.

📜 Prompt Name: Meeting Summary Scribe

Prompt:
Distill meetings into concise summaries including discussion topics, key takeaways, and action items.

Your task is to review the provided meeting notes and create a concise summary that captures the essential information, focusing on key takeaways and action items assigned to specific individuals or departments during the meeting. Use clear and professional language, and organize the summary in a logical manner using appropriate formatting such as headings, subheadings, and bullet points.

Ensure that the summary is easy to understand and provides a comprehensive but succinct overview of the meeting’s content, with a particular focus on clearly indicating who is responsible for each action item.

Google, Harvard, and even OpenAI are offering FREE Generative AI courses (no payment required) 🎓

Here are 8 FREE courses to master AI in 2024:

1. Google AI Courses
5 courses covering generative AI from the ground up
https://www.cloudskillsboost.google/paths/118

2. Microsoft AI Course
Basics of AI, neural networks, and deep learning
https://microsoft.github.io/AI-For-Beginners/

3. Introduction to AI with Python (Harvard)
7-week course exploring AI concepts and algorithms
https://www.edx.org/learn/artificial-intelligence/harvard-university-cs50-s-introduction-to-artificial-intelligence-with-python

4. ChatGPT Prompt Engineering for Devs (OpenAI & DeepLearning)
Best practices and hands-on prompting experience
https://www.deeplearning.ai/short-courses/chatgpt-prompt-engineering-for-developers/

5. LLMOps (Google Cloud & DeepLearning)
Learn the LLMOps pipeline and deploy custom LLMs
https://www.deeplearning.ai/short-courses/llmops/

Aws vs azure vs google cloud

Qualcomm
Engineer hiring Gen AI,QA

Apply link: https://cuvette.tech/app/other-jobs/678a3bca76eb5643b226f1d2?referralCode=8T994D

All the best 👍👍

Foundational Large Language Models & Text Generation

Advanced ChatGPT Prompting Techniques

1. Iterative Refinement
Refine prompts step by step, using feedback to improve response accuracy. Example: Start broad, then ask for more specific details.

2. Contextual Memory
Build continuity across conversations by referencing past interactions. Example: "Earlier, you explained X. Can you elaborate on Y?"

3. Multi-Turn Dialogues
Engage in layered conversations, allowing responses to build upon each other. Example: "What is AI?" → "Explain machine learning in detail."

4. Task-Specific Prompts
Customize prompts for targeted tasks like summarization, translation, or code generation. Example: "Summarize this article on AI ethics."

5. Guided Exploration
Direct ChatGPT to focus on specific areas of interest. Example: "Discuss the ethical considerations of AI in healthcare."

6. Prompt Chaining
Use a sequence of related prompts, where each builds on the previous one. Example: "Explain the basics of AI" → "How does it differ from traditional programming?"

Mastering LLM Engineering: The Complete Roadmap

1️⃣ LLM Fundamentals

Learn to work with pre-trained LLMs and open-source models for language tasks.

2️⃣ Vector Databases

Master storing, managing, and querying text data using embeddings and vector databases.

3️⃣ RAG Basics

Build retrieval-augmented generation pipelines with tools like LangChain and LlamaIndex.

4️⃣ LLM Deployment

Deploy LLMs to cloud or edge, monitor performance, and ensure ethical AI practices.

5️⃣ Advanced RAG

Master advanced retrieval, embeddings, and LLM evaluation techniques.

6️⃣ Inference Optimization

Optimize LLMs with quantization, pruning, and SLMs.

#genai