It’s important to understand the difference between Prompt-Engineering, Fine-Tuning, and Retrieval Augmentation as they are all great methods to incorporate domain knowledge into your Generative AI application. 

Prompt-Engineering is all about in-context learning. This is particularly good when you have a static situation and you can iterate on it until you get the answer you want. https://learn.microsoft.com/en-us/azure/ai-services/openai/concepts/advanced-prompt-engineering 

Fine-Tuning is good to learn skills or new jargon, or ways to produce responses and are pernament to your LLM. To Fine Tune or Not Fine Tune? That is the question (youtube.com) 

Retrieval-Augmentation is good for learning new facts by grounding your prompt with the relevant information it needs on the fly to answer a question. RAG and generative AI - Azure AI Search | Microsoft Learn 

To your question, storing user queries in a vector database is a strategy that can significantly enhance the model's performance over time if they are relevant to the task the LLM is trying to solve. If the user queries are NOT relevant, and you’re leveraging them as history to maintain in your user prompt, you’re likely just providing the LLM noise.  

Hope this helps!

This is a fantastic question! Small Language Models (SLMs) are very new and there is lots of research going on to find out more about their use case. Here are a few articles from Microsoft that talk about them more in depth: Phi-2: The surprising power of small language models - Microsoft Research and Orca 2: Teaching Small Language Models How to Reason - Microsoft Research.  

My takeaways from the research are the following: 

1. Specialized Task Handling: SLMs like Phi-2 can be tailored for specific domains or tasks such as legal document analysis or technical support queries. In a RAG setup, these models can act as specialized agents that handle particular types of queries with high precision, complementing the broader knowledge base of LLMs. 
2. Efficient Re-Ranking: SLMs can be used to quickly re-rank the results retrieved by an initial search query based on more nuanced criteria or domain-specific knowledge. An SLM trained in specific criteria can refine and re-rank results before presenting them to the user, improving relevance and accuracy. Note, Azure AI Search has a re-ranker that is SOTA already 🙂 Semantic ranking - Azure AI Search | Microsoft Learn
3. Augmenting Search Queries: Before executing a search query, an SLM could preprocess the query to better capture the user’s intent or expand it with additional relevant terms. 
4. Post-Search Query Analysis: After initial results are retrieved and presented, SLMs can offer an additional layer of interaction, where they analyze user feedback or follow-up questions to refine the search results further.  

I think the integration of SLMs with LLMs in an RAG architecture offers a pretty compelling approach to creating more efficient, accurate, and context-aware search and information retrieval systems. However, this space is pretty new and we’re all continuously experimenting to see how SLMs work best in the context of RAG. Hopefully, more to come soon! 

Hi Vivek It really depends on how much control and customization you want. The specific technology powering the platforms is generally the same.

The intent for Azure AI Studio is that it will have parity features with Azure OpenAI Studio, and at some point, replace Azure OpenAI studio.

The intent is for Copilot Studio to replace Azure OpenAI Studio --> chat completion --> deploy to PVA.

I believe feature/capability is already at parity, Copilot Studio may have more available capabilities now.

From a RAG perspective, Azure AI Studio, Azure OpenAI Studio and Copilot Studio all use the same feature: Azure OpenAI on your data. This makes it easy to quickly ground your app with your own data.

If you require multitenancy, you should go with AI Studio. If you're building in a single tenant environment, you should go with Copilot Studio.

If you are building a custom copilot for internal users (e.g. an human resources knowledge base), business operations or creating a customer engagement solution, you should probably start with Copilot Studio.

If your chatbot will be using significant M365 data sources like Sharepoint and Onedrive, you should start with Copilot Studio. If you will be connecting to OneLake, blob storage or other databases, you should start with AI Studio.

You technically can connect any data source for both Copilot and AI Studio, but one studio has an easier experience for certain data. 

Copilot studio, as you mentioned is a low code based platform, and provide out of the box chatbot interfaces/apps to use.

Azure AI Studio is in preview, as is the SDK, so you may prefer starting with a solution accelerator if you prefer to code.

Hopefully this helps - let me know what tech specifically you are interested in, and I can respond

You have options across Azure to accomplish report summarization.  Within the context of RAG and Azure AI Search, you can use prompt engineering techniques to accomplish your desired outcome by giving the LLM specific instructions to summarize retrieved documents.  Relevant documentation on prompt engineering for Azure OpenAI is here.  Within Azure AI Services Language Service there is also an API with native document support for summarization (please see this announcement). The right detailed steps are going to be dependent on exactly what you are trying to accomplish in terms of summarization (extractive, generative, etc.) and where you are starting from (already have the report document, must retrieve it first, etc.).