As developers, we're always on the hunt for new and exciting tools. Recently, few AI solutions have caught my eye. Let's explore what these solutions offer and how you, as a fellow developer, can make use of them.
Microsoft's Solution: ChatGPT with Azure Cognitive Search
Learn More About Microsoft's Solution
Microsoft introduces Azure Cognitive Search and Azure OpenAI Service. This synergy allows developers to create solutions related to ChatGPT, but powered by your proprietary data. By using Azure's robust enterprise features, Cognitive Search's adeptness at indexing and retrieving data, and ChatGPT's natural language interaction capabilities, businesses can create dynamic conversational experiences rooted in their own knowledge bases.
Amazon's Solution: Generative AI with Kendra and LangChain
Learn More About Amazon's Solution
AWS presents a solution for Generative AI applications using Amazon Kendra, large language models (LLMs), and the LangChain framework. Kendra retrieves relevant enterprise data, which is then processed by LLMs to produce accurate responses, all orchestrated through LangChain.
Google's Solution: Generative AI with Vertex AI PaLM 2 and LangChain
Learn More About Google's Solution
Google Cloud offers approach for Generative AI applications by integrating Vertex AI PaLM 2 models with the LangChain framework. The Vertex AI PaLM models, tailored for text and chat interactions, work in tandem with LangChain, allowing Large Language Models (LLMs) to interact with external systems, such as databases or Google Search. This ensures that the AI not only understands but also reasons with data in a context-rich manner.
All three solutions emphasize the use of proprietary data with RAG technique. While each solution has its unique features and benefits, they all ensure that AI not only understands but also reasons with data in a context-rich manner. This shared focus on RAG underscores its significance in the current AI landscape and its potential to change how we interact with and leverage AI.
Innovation: The Fridge Story
Who makes the most from fridges? Not the sellers, but Coca-Cola - they make products for fridges. This story shows how we developers can use AI tools, like a fridge, to make new things.
Like Coca-Cola used fridges, we can use AI for new ideas. The secret is how you use and change it for new challenges.
LLM Techniques
Fine-Tuning
Fine-tuning is the process of taking a pre-trained model and training it further on a smaller, specific dataset. This tailors the model's responses to particular tasks or domains, enhancing its performance on that specific data.
- Good for changing to fit new data.
- Makes things better but might cost more.
- Use the latest data for your business.
Retrieval Augmented Generation (RAG)
RAG combines the power of large language models with external knowledge retrieval. It first fetches relevant documents or passages from a database and then generates a response based on the retrieved information and the input query.
- Another way to learn from new data.
- Think over new data without spending much.
- Update regularly and check facts.
Prompt engineering
Prompting involves feeding a model a specific input or "prompt" to guide its output. It's a way to interact with pre-trained models, like asking a question and receiving an answer, without additional training.
By knowing these techniques, we can get more from AI, making new apps that keep up with changes in data and tech.
Let's try it
The world of AI has much to offer, and Microsoft's, Google's and Amazon's solutions are ready for you to try. Why not jump in and see what you can make?
Microsoft's Solution GitHub repo
Amazon's Solution GitHub repo
Google's Solution GitHub repo
I'm trying these solutions myself and will share what I find. Stay tuned, and let's see where our creativity takes us.
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