Basic Generative AI & Search Engines

Dig deeper into Natural Language Processing, Language Models, and Search Engine Usage Techniques
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This course is a foundational exploration of Generative AI and its synergy with search engines. It delves into the fundamentals of Natural Language Processing (NLPs) & language models and uncovers how Generative AI harnesses NLP’s power. It will also help learners effective search engine usage techniques and understand the differences between using Google Search and Generative AI platforms. The course aims to shed light on the collaborative relationships between Generative AI and search engines, enabling them to make informed decisions on leveraging AI-driven insights for improved digital interactions and innovative strategies. 

Why this

This course will help you learn: 

  • Fundamentals of Natural Language Processing 
  • Fundamentals of Language Models 
  • How Generative AI leverages Natural Language Processing 
  • Effective search engine usage 
  • Google vs. Generative AI platforms 


  • What is Natural Language Processing?
  • NLP terminology
    • Corpus
    • Tokenization
    • Word embeddings
  • Subdomains of NLP
    • Text classification
    • Named Entity Recognition
    • Part of Speech tagging
    • Machine translation
    • Information Extraction
    • Question Answering
    • Text generation
    • Sentiment analysis
    • Co-reference resolution
  • Demo & review of text classification

  • What are Language Models? 
  • Types of Language Models: 
    • N-Gram
    • Feedforward
    • Recurrent Neural Networks (RNN)
      • Long Short Term Memory (LSTM) 
    • Transformer 
  • Types of AIs and Architecture 

  • What is Generative AI? 
  • Differences between Generative AI and Natural Language Processing 
  • Importance of Natural Language Processing in Generative AI 
  • NLP key features within Generative AI: 
    • Tokenization
    • Word Embeddings 
    • Language Modelling 
    • Attention Mechanism 
    • Transformation Architecture 
  • NLP techniques use cases 

  • Researching and fact-checking Generative AI results 
  • Best practices/techniques for using search engines 
    • Mandate specific terms 
    • Exclude specific terms 
    • Site-specific searches 
    • File-specific searches 
    • Date-restricted searches 
  • Search engine techniques demo 

  • Difference between a Search Engine & Generative AI 
  • When should you be using Generative AI vs. Search Engine 
  • Use cases for Generative AI and search engines demo 

  • Test the effectiveness of NLPs within GenAI tools like ChatGPT 
  • Create “Spark Notes” on short stories 
  • Use NLPs to find all key pieces of information needed to understand the story fully 
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Having a STEM background would be helpful.


Generative AI is a subset of AI that focuses on understanding patterns and structure in data and then using that to create more data like it.

Until now, machines couldn’t exhibit behavior that was indistinguishable from human responses. Generative AI has made that possible. In a world that operates on the principle of ‘Survival of the Fittest’, embracing Generative AI could be the edge you need to boost your productivity, improve operations, be more reliable and consistent, and do so much more.

Yes, absolutely! The Basic Generative AI and Search Engines online course is a virtual, instructor-led program so you can enroll and learn from anywhere.

This course is designed to be highly hands-on for all learners. Roughly 70% of this course would be hands-on practical training while 30% would be theoretical studies.

Yes! There will be a pre- and post-assessment for every module during this Generative AI online training course.

Interested in this Course?

    Ready to recode your DNA for GenAI?
    Discover how Cognixia can help.

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