DeepSeek LLM Fundamentals Training in San Francisco, USA

The DeepSeek LLM Fundamentals Training provides a comprehensive introduction to Large Language Models (LLMs) using DeepSeek AI models.
The course is designed to help participants understand how LLMs work, what they can do, and how to use them responsibly and effectively across business, research, and technical environments.

This course builds strong conceptual foundations and practical skills for working with DeepSeek LLMs.

Introduction to LLMs & Generative AI

• What are Large Language Models?

• How Generative AI works (high-level)

• Evolution of LLMs

• Common use cases across industries

Overview of DeepSeek LLMs

• What is DeepSeek AI?

• DeepSeek model families (high-level)

• Capabilities and positioning

• Comparison with other LLM platforms (conceptual)

How LLMs Work

• Tokens and tokenization

• Training data concepts

• Pre-training vs fine-tuning

• Inference and generation basics

What LLMs Can & Cannot Do

• Strengths of LLMs

• Common failure modes

• Hallucinations and confidence issues

• When NOT to use LLMs

Getting Started with DeepSeek LLMs

• Interfaces and access methods

• Basic usage workflows

• Typical task examples

• Understanding response variability

Prompting Fundamentals

• Writing clear instructions

• Context and constraints

• Asking for format and tone

• Simple prompt patterns

Core LLM Use Cases

• Text generation

• Summarization

• Q&A and information extraction

• Translation and rewriting

Evaluating LLM Outputs

• Checking accuracy

• Detecting hallucinations

• Human review workflows

• Quality control practices

Bias, Fairness & Ethical Considerations

• Understanding bias in AI

• Fairness and inclusivity

• Ethical use of LLMs

• Transparency in AI-assisted work

Security, Privacy & Data Protection

• Handling sensitive data

• What not to share with LLMs

• Organizational AI policies

• Basic governance concepts

Introduction to LLMs in Business & Research

• Business productivity use cases

• Research and education support

• Government and public sector usage

• Enterprise AI adoption patterns

Intro to Advanced LLM Concepts

• Retrieval-Augmented Generation (RAG)

• AI agents (conceptual)

• Multimodal AI (conceptual)

• Fine-tuning vs prompting

Hands-on Exercises

Summary and Conclusion