Domain-specific LLMs, RAG AI & Prompt Engineering
Et Tu Code
Unlock the full potential of language models with Domain-specific LLMs, RAG AI & Prompt Engineering! This comprehensive guide covers everything you need to know to build custom Language Models, Retrieval Augmented Generative AI, and fine-tune prompt engineering. Learn how to tailor your models to specific domains, improve their performance with retrieval augmentation, and create effective prompts for maximum generation accuracy. With practical examples and exercises throughout the book, you'll be able to apply these techniques to real-world applications. Whether you're a seasoned AI developer or just starting out, this guide is essential for anyone looking to push the boundaries of language modeling. Get ready to take your language models to the next level!
Duration - 10h 3m.
Author - Et Tu Code.
Narrator - Helen Green.
Published Date - Tuesday, 02 January 2024.
Copyright - © 2024 Et Tu Code ©.
Location:
United States
Description:
Unlock the full potential of language models with Domain-specific LLMs, RAG AI & Prompt Engineering! This comprehensive guide covers everything you need to know to build custom Language Models, Retrieval Augmented Generative AI, and fine-tune prompt engineering. Learn how to tailor your models to specific domains, improve their performance with retrieval augmentation, and create effective prompts for maximum generation accuracy. With practical examples and exercises throughout the book, you'll be able to apply these techniques to real-world applications. Whether you're a seasoned AI developer or just starting out, this guide is essential for anyone looking to push the boundaries of language modeling. Get ready to take your language models to the next level! Duration - 10h 3m. Author - Et Tu Code. Narrator - Helen Green. Published Date - Tuesday, 02 January 2024. Copyright - © 2024 Et Tu Code ©.
Language:
English
Opening Credits
Duración:00:02:07
Preface
Duración:00:03:27
Part 1 domain specific llms
Duración:00:00:16
Introduction to domain specific llms
Duración:00:06:05
Key concepts in domain specific llms
Duración:00:06:57
Applications across industries
Duración:00:04:53
Creating custom llms for specific domains
Duración:00:05:08
Steps to create domain specific language models
Duración:00:04:02
Steps to create domain specific language models define the domain and objectives
Duración:00:03:22
Steps to create domain specific language models collect and curate domain specific data
Duración:00:06:20
Steps to create domain specific language models pre process and cleanse data
Duración:00:08:18
Steps to create domain specific language models select an architecture or pre trained model
Duración:00:05:08
Steps to create domain specific language models fine tune the model
Duración:00:05:57
Steps to create domain specific language models evaluate model performance
Duración:00:06:01
Steps to create domain specific language models iterative refinement
Duración:00:04:17
Steps to create domain specific language models optimize for efficiency and resource usage
Duración:00:06:33
Steps to create domain specific language models document and share guidelines
Duración:00:04:46
Steps to create domain specific language models deploy and monitor
Duración:00:04:34
Popular domain specific language models
Duración:00:03:00
Popular domain specific language models medicalbert
Duración:00:05:29
Popular domain specific language models legalgpt
Duración:00:04:41
Popular domain specific language models codebert
Duración:00:04:13
Popular domain specific language models finbert
Duración:00:04:49
Popular domain specific language models scibert
Duración:00:03:31
Popular domain specific language models hrchatbot
Duración:00:03:48
Popular domain specific language models tourismtalk
Duración:00:03:47
Popular domain specific language models ecotechai
Duración:00:01:36
Popular domain specific language models edunlp
Duración:00:04:35
Fine tune an llm for domain specific needs
Duración:00:06:19
Challenges and solutions
Duración:00:04:54
Integration with existing systems
Duración:00:06:11
Case studies
Duración:00:00:14
Best practices for training an llm
Duración:00:04:02
Best practices for training an llm start small
Duración:00:04:50
Best practices for training an llm understand scaling laws
Duración:00:04:53
Best practices for training an llm prioritize data quality
Duración:00:05:23
Best practices for training an llm enforce data security and privacy
Duración:00:03:38
Best practices for training an llm monitor and evaluate model performance
Duración:00:05:15
Faqs on domain specific llm
Duración:00:06:50
Faqs on domain specific llm how do i choose the right domain for my llm?
Duración:00:04:04
Faqs on domain specific llm what preprocessing steps are essential for domain specific llms?
Duración:00:06:32
Faqs on domain specific llm how can i fine tune an existing llm for a specific domain?
Duración:00:04:22
Faqs on domain specific llm what challenges are commonly faced in domain specific llm projects?
Duración:00:05:28
Faqs on domain specific llm how can domain specific llms contribute to industry specific applications?
Duración:00:04:29
Future trends and innovations
Duración:00:05:15
Ethical considerations
Duración:00:05:56
Resources and tools
Duración:00:06:09
Guidelines for model evaluation
Duración:00:04:50
Collaboration in language model development
Duración:00:05:22
Leveraging pre trained models for efficiency
Duración:00:05:03
Innovations in natural language understanding
Duración:00:05:09
Scaling up: large scale language models
Duración:00:05:30
Security considerations in language model deployment
Duración:00:05:55
Hugging face domain specific llms
Duración:00:03:54
Hugging face domain specific llms financeconnect 13b
Duración:00:01:18
Hugging face domain specific llms llama2 7b aivision360
Duración:00:05:36
Hugging face domain specific llms llama2 13b aivision360
Duración:00:05:50
Part 2 retrieval augmented generation
Duración:00:00:16
Introduction to rag
Duración:00:04:39
Understanding retrieval models
Duración:00:04:58
Generative language models
Duración:00:05:01
Rag architecture
Duración:00:04:47
Applications of rag
Duración:00:05:20
Fine tuning and customization
Duración:00:04:43
Challenges and considerations
Duración:00:05:16
Future trends in rag
Duración:00:05:51
Rag best practices
Duración:00:03:27
Popular applications of rag ai
Duración:00:06:07
Popular applications of rag ai content creation
Duración:00:04:15
Popular applications of rag ai question answering systems
Duración:00:04:16
Popular applications of rag ai chatbots and virtual assistants
Duración:00:05:34
Popular applications of rag ai knowledge base expansion
Duración:00:05:27
Popular applications of rag ai medical diagnosis support
Duración:00:04:00
Creating rag ai from scratch
Duración:00:05:22
Creating rag ai from scratch data collection and preprocessing
Duración:00:03:54
Creating rag ai from scratch building the retrieval system
Duración:00:05:59
Creating rag ai from scratch implementing the generation component
Duración:00:06:12
Creating rag ai from scratch integrating retrieval and generation
Duración:00:04:30
Creating rag ai from scratch training and fine tuning
Duración:00:05:57
Rag ai project examples
Duración:00:06:55
Rag ai project examples medical diagnosis assistant
Duración:00:04:03
Rag ai project examples legal document summarizer
Duración:00:05:19
Rag ai project examples code assistance tool
Duración:00:04:32
Rag ai project examples educational q&a system
Duración:00:06:14
Cloud support for retrieval augmented generation (rag) ai
Duración:00:04:00
Cloud support for retrieval augmented generation (rag) ai amazon web services (aws)
Duración:00:04:34
Cloud support for retrieval augmented generation (rag) ai microsoft azure
Duración:00:06:19
Cloud support for retrieval augmented generation (rag) ai google cloud platform (gcp)
Duración:00:03:36
Cloud support for retrieval augmented generation (rag) ai ibm cloud
Duración:00:05:02
Cloud support for retrieval augmented generation (rag) ai oracle cloud infrastructure (oci)
Duración:00:05:21
Multimodal rag
Duración:00:05:10
Cross language rag
Duración:00:05:29
Dynamic contextualization
Duración:00:05:36
Rag in real time applications
Duración:00:06:00
Ethical considerations in rag
Duración:00:05:16
Conclusion: mastering rag
Duración:00:05:56
Part 3 prompt engineering
Duración:00:00:15
Introduction to prompt engineering
Duración:00:05:03
The psychology of prompts
Duración:00:04:21
The psychology of prompts cognitive science principles
Duración:00:07:37
The psychology of prompts language nuances
Duración:00:04:23
Building effective prompts
Duración:00:05:25
Adapting prompts for different models
Duración:00:05:02
Evaluating prompt performance
Duración:00:05:14
Advanced techniques in prompt engineering
Duración:00:07:20
Advanced techniques in prompt engineering transfer learning with prompts
Duración:00:05:50
Advanced techniques in prompt engineering multimodal prompt engineering
Duración:00:01:08
Mathematics underpinning efficient prompt engineering
Duración:00:05:33
Mathematics underpinning efficient prompt engineering linear algebra in prompt design
Duración:00:05:03
Mathematics underpinning efficient prompt engineering probability and statistical modeling
Duración:00:04:21
Mathematics underpinning efficient prompt engineering optimization algorithms for prompt tuning
Duración:00:05:08
Mathematics underpinning efficient prompt engineering information theory in prompt compression
Duración:00:05:32
Popular chatgpt prompts
Duración:00:03:24
Popular chatgpt prompts creative writing
Duración:00:04:59
Popular chatgpt prompts programming assistance
Duración:00:04:07
Popular chatgpt prompts educational queries
Duración:00:05:00
Popular chatgpt prompts technical documentation generation
Duración:00:05:02
The future of prompt engineering
Duración:00:05:06
Interactive prompt design
Duración:00:05:11
Challenges and solutions in prompt engineering
Duración:00:05:24
Collaborative prompt design
Duración:00:05:46
Practical applications and case studies
Duración:00:04:24
Glossary
Duración:00:05:24
Bibliography
Duración:00:04:09
Ending Credits
Duración:00:01:51