Vote of Thanks
Thanks to Ayyanar Jeyakrishnan for provididing this content at The Meetup
Path to GenAI
we came from descriptive analytics and then to predictive analytics and thne to word embeddings
and then we have generative ai now.
Let's Understand Basics
A word is tokenized first and then embedded as a vector and then we can apply the alogrithms that we want to apply like semantic search or clustering.
Foundation Models that are available on AWS Sagemaker to jumpstart for self managed access.
Publicaly available
- Stability AI
- Alexa
- HuggingFace Models
Properitary Models
- Co:here
- Lighten
- A121 Labs
Generative AI on AWS
- AWS Bedrock
- Amazon EC2 Trn1n and Amazon EC2 inf2
- Amazon Code whisperer
Amazon Bedrock Foundation Models
- AI21 labs - (Jurassic-2)
- ANTHROPIC - (Claude)
- Stability AI - (stable diffusion)
- Amazon - (Amazon Titan)
on AWS we can try connecting with LLM by going through the Sagemaker foundation model - playground console
Things we can do by prompting and only using LLM
- Text Generation
- Summarization
- Translation
- Code Generation
- Question and Answering
Choice of Choosing LLM should depend on these four things
- Quality
- Cost
- Latency
- Customization
Prompting Tricks
Zero shot Prompting - Decribing task that LLM needs to do without providing any examples
One Shot Prompting - Describing Task that LLM Needs to do along with one example
Few Shot Prompting - Describing Task along with Some examples to rely on
Note: Few shot > one shot > zero shot prompting
Challenges that we have while adapting LLM's to Enterprises
- Pretraining Model from scratch
- Data Privacy and Security and Ethics
- Model Interpretability and hallucination
- Fine Tuning and Customization
- Continual Model Improvement and Integration.
Top comments (0)