more openai content

Author: crpietschmann

02_Overview_Azure_OpenAI/README.md

@@ -35,7 +35,7 @@ Azure OpenAI stores and processes data to provide the service and to monitor for
 Here are a few important things to know in regards to the security and privacy of your prompts (inputs) and completions (outputs), your embeddings, and your training data when using Azure OpenAI:
 
 - are NOT available to other customers.
-- are NOT availabe to OpenAI.
+- are NOT available to OpenAI.
 - are NOT used to improve OpenAI models.
 - are NOT used to improve any Microsoft or 3rd party products or services.
 - are NOT used for automatically improving Azure OpenAI models for your use in your resource (The models are stateless, unless you explicitly fine-tune models with your training data).

03_Overview_AI_Concepts/README.md

@@ -57,15 +57,17 @@ https://learn.microsoft.com/en-us/azure/ai-services/openai/concepts/advanced-pro
 
 ### ReAct
 
-### Others?
+Reasoning and Acting (ReAct) leverages LLMs to generate both reasoning traces and task-specific actions. ReAct combines these to allow for greater synergy between the two. Reasoning traces help the model induce, track, and update action plans as well as handle exceptions, while actions allow it to interface with external data sources to gather additional information. When using LLMs to answer questions and verify facts, ReAct is used to overcome issues of hallucination and error propagation by interacting with the additional usage of external data sources.
+
+### Others
 
 #### Zero-shot prompting
 
 Zero-shot prompting is what we would consider the “default”. This is when we provide no examples of inputs/expected outputs to the model to work with. We’re leaving it up to the model to decipher what is needed and how to output it from the instructions.
 
 #### Few-shot prompting
 
-Few-shot prompting provides examples to guide the model to the desired output.
+Few-shot prompting is a technique that enables in-context learning for the LLM by providing examples or further information within the prompt to steer the model to generate a better response. Providing additional information in the prompt helps particularly in areas where the data used to train the model isn't enough to generate the desired output from the model.
 
 https://learn.microsoft.com/en-us/azure/ai-services/openai/concepts/advanced-prompt-engineering?pivots=programming-language-chat-completions#provide-grounding-context
 

07_Explore_OpenAI_models/README.md

@@ -1,9 +1,40 @@
 # Explore the Azure OpenAI models and endpoints (console app)
 
-## Selecting an LLM
+## Azure OpenAI Models
+
+Azure OpenAI is powered by a diverse set of models with different capabilities.
+
+| Model | Description |
+| -- | --- |
+| GPT-4 | A set of models that improve on GPT-3.5 and can understand and generate natural language and code. |
+| GPT-3.5 | A set of models that improve on GPT-3 and can understand and generate natural language and code. |
+| Embeddings | A set of models that can convert text into numerical vector form to facilitate text similarity. |
+| DALL-E | A series of models in preview that can generate original images from natural language. |
+| Whisper | A series of models in preview that can transcribe and translate speech to text. |
+
+### GPT-4 and GPT-3.5 Models
+
+GPT-4 can solve difficult problems with greater accuracy than any of OpenAI's previous models. Like GPT-3.5 Turbo, GPT-4 is optimized for chat and works well for traditional completions tasks.
+
+The GPT-35-Turbo and GPT-4 models are language models that are optimized for conversational interfaces. The models behave differently than the older GPT-3 models. Previous models were text-in and text-out, meaning they accepted a prompt string and returned a completion to append to the prompt. However, the GPT-35-Turbo and GPT-4 models are conversation-in and message-out. The models expect input formatted in a specific chat-like transcript format, and return a completion that represents a model-written message in the chat. While this format was designed specifically for multi-turn conversations, you'll find it can also work well for non-chat scenarios too.
+
+https://learn.microsoft.com/en-us/azure/ai-services/openai/how-to/chatgpt?tabs=python&pivots=programming-language-chat-completions
 
 https://learn.microsoft.com/en-us/semantic-kernel/prompt-engineering/llm-models
 
+https://learn.microsoft.com/en-us/azure/ai-services/openai/concepts/models
+
+### DALL-E
+
+The DALL-E model, enables the use of a text prompt provided by a user as the input that the model then uses to generate an image response.
+
+## Selecting an LLM
+
+Before a Large Language Model (LLM) can be implemented into a solution, the specific LLM to use must be chosen.
+
+
+
+
 
 ## Explore and use models from code