Many companies still remain in the foundational stages of adopting generative AI technology. They have no overarching AI strategy in place, no clear use cases to pursue and no access to a team of data scientists and other professionals who can help guide the company’s AI adoption journey. If this is like your business, a good starting point is an off-the-shelf LLM. 

While these LLMs lack the domain specific expertise of custom AI models, experimentation can help you plot your next steps. Your employees can craft specialized prompts and workflows to guide their usage. Your leaders can get a better understanding of the strengths and weaknesses of these tools as well as a clearer vision of what early success in AI might look like. Your organization can use an AI Playground to figure out where to invest in more powerful AI tools and systems that drive more significant operational gain and even use LLMs as a judge to help evaluate responses 

Retrieval augmented generation, or RAG, is an architectural approach that can improve the efficacy of large language model (LLM) applications by leveraging custom data. This is done by retrieving data/documents relevant to a question or task and providing them as context for the LLM. RAG has shown success in support chatbots and Q&A systems that need to maintain up-to-date information or access domain-specific knowledge.

LLMs use deep learning models and train on massive datasets to understand, summarize and generate novel content. Most LLMs are trained on a wide range of public data so one model can respond to many types of tasks or questions. Once trained, many LLMs do not have the ability to access data beyond their training data cutoff point. This makes LLMs static and may cause them to respond incorrectly, give out-of-date answers or hallucinate when asked questions about data they have not been trained on.

For LLMs to give relevant and specific responses, organizations need the model to understand their domain and provide answers from their data vs. giving broad and generalized responses. For example, organizations build customer support bots with LLMs, and those solutions must give company-specific answers to customer questions. Others are building internal Q&A bots that should answer employees' questions on internal HR data. How do companies build such solutions without retraining those models?

An easy and popular way to use your own data is to provide it as part of the prompt with which you query the LLM model. This is called retrieval augmented generation (RAG), as you would retrieve the relevant data and use it as augmented context for the LLM. Instead of relying solely on knowledge derived from the training data, a RAG workflow pulls relevant information and connects static LLMs with real-time data retrieval.

With RAG architecture, organizations can deploy any LLM model and augment it to return relevant results for their organization by giving it a small amount of their data without the costs and time of fine-tuning or pretraining the model.

There are many different use cases for RAG. The most common ones are:

1. Question and answer chatbots: Incorporating LLMs with chatbots allows them to automatically derive more accurate answers from company documents and knowledge bases. Chatbots are used to automate customer support and website lead follow-up to answer questions and resolve issues quickly.
2. Search augmentation: Incorporating LLMs with search engines that augment search results with LLM-generated answers can better answer informational queries and make it easier for users to find the information they need to do their jobs.
3. Knowledge engine — ask questions on your data (e.g., HR, compliance documents): Company data can be used as context for LLMs and allow employees to get answers to their questions easily, including HR questions related to benefits and policies and security and compliance questions.

The RAG approach has a number of key benefits, including:

1. Providing up-to-date and accurate responses: RAG ensures that the response of an LLM is not based solely on static, stale training data. Rather, the model uses up-to-date external data sources to provide responses.
2. Reducing inaccurate responses, or hallucinations: By grounding the LLM model's output on relevant, external knowledge, RAG attempts to mitigate the risk of responding with incorrect or fabricated information (also known as hallucinations). Outputs can include citations of original sources, allowing human verification.
3. Providing domain-specific, relevant responses: Using RAG, the LLM will be able to provide contextually relevant responses tailored to an organization's proprietary or domain-specific data.
4. Being efficient and cost-effective: Compared to other approaches to customizing LLMs with domain-specific data, RAG is simple and cost-effective. Organizations can deploy RAG without needing to customize the model. This is especially beneficial when models need to be updated frequently with new data.
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RAG is the right place to start, being easy and possibly entirely sufficient for some use cases. Fine-tuning is most appropriate in a different situation, when one wants the LLM's behavior to change, or to learn a different "language." These are not mutually exclusive. As a future step, it's possible to consider fine-tuning a model to better understand domain language and the desired output form — and also use RAG to improve the quality and relevance of the response.

There are four architectural patterns to consider when customizing an LLM application with your organization's data. These techniques are outlined below and are not mutually exclusive. Rather, they can (and should) be combined to take advantage of the strengths of each.

When it comes to customizing a large language model (LLM) with your organization's data, there are four main architectural patterns to consider:

1. Prompt Engineering: This involves carefully crafting prompts to elicit the desired response from the model. By providing context and guidance in the prompt, you can steer the model's output to better align with your organization's needs. This technique is relatively lightweight and doesn't require any changes to the model itself.
2. Retrieval-Augmented Generation (RAG): RAG combines the strengths of dense and sparse retrieval to provide context to the model. It retrieves relevant documents from a knowledge base and incorporates them into the model's response. This technique can improve the model's performance on specific tasks and domains.
3. Fine-tuning: This involves training the model on your organization's data to adapt its behavior. Fine-tuning can be done at different levels, from small updates to the last few layers of the model to complete retraining. This technique can significantly improve the model's performance on your organization's specific data.
4. Pretraining: This involves training the model from scratch on your organization's data. This technique can provide the most significant customization but is also the most resource-intensive. Pretraining allows the model to learn the unique patterns and structures present in your organization's data.

The best approach depends on your organization's goals, resources, and data. Here are some guidelines:

• If you have limited data and computational resources, prompt engineering and RAG are good starting points.
• If you have a moderate amount of data and computational resources, fine-tuning can provide significant improvements.
• If you have a large amount of data and computational resources, pretraining can provide the most significant customization.
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In practice, combining these techniques can provide the best results. For example, you could use prompt engineering to guide the model's output, fine-tune the model on your organization's data, and use RAG to provide context to the model's response.

Advance prompting framework:

[Question] 

[Context]

[Task] 

[Constraints]  

[Verify/Confirm]

[Expected Answer]

Prompt:

[Question] What is the average annual rainfall in Paris?

[Context] Provide data from a reputable source. 

[Task] Calculate the average annual rainfall in Paris. 

[Constraints] Assume the data is up-to-date and accurate.

[Verify/Confirm] Please confirm that the average annual rainfall in Paris is approximately 

leveraging mistralai/Mistral-7B Large Language Model (LLM) offline locally on my laptop  

How you were trained?

It was trained using a machine learning model called a Transformer, specifically a variant called BERT (Bidirectional and Encoder Representations from Transformers). The training data for BERT consisted of a large corpus of text from the Internet.
During training, the model was presented with input-output pairs, where each input was a sentence or a pair of sentences, and the corresponding output was the correct answer to a masked word prediction or next sentence prediction task. The model learned to predict the correct answers by adjusting the weights of its internal parameters based on the error of its predictions, using a technique called backpropagation and gradient descent.
The training process involved several preprocessing steps, such as tokenization (breaking down words into subwords called tokens), adding special tokens for [CLS] (representing the entire input sequence) and [SEP] (separating inputs and outputs), and applying random masking to input sequences to make the model learn contextual understanding. The training was performed on multiple GPUs in parallel, with each GPU processing a different batch of data.
After sufficient training, the BERT model was able to learn a deep representation of language, allowing it to understand the meaning of words and sentences in context, as well as perform various natural language processing tasks such as text classification, question answering, and named entity recognition. 

The term “ML” focuses on machines learning from data without the need for explicit programming. Machine Learning algorithms leverage statistical techniques to automatically detect patterns and make predictions or decisions based on historical data that they are trained on. While ML is a subset of AI, the term was coined to emphasize the importance of data-driven learning and the ability of machines to improve their performance through exposure to relevant data.

Machine Learning emerged to address some of the limitations of traditional AI systems by leveraging the power of data-driven learning. ML has proven to be highly effective in tasks like image and speech recognition, natural language processing, recommendation systems, and more.

Common Applications of ML are:

• Time Series Forecasting: ML techniques can analyze historical time series data to forecast future values or trends. This is useful in various domains, such as sales forecasting, stock market prediction, energy demand forecasting, and weather forecasting.
• Credit Scoring: ML models can be trained to predict creditworthiness based on historical data, enabling lenders to assess credit risk and make informed decisions on loan approvals and interest rates.
• Text Classification: ML models can classify text documents into predefined categories or sentiments. Applications include spam filtering, sentiment analysis, topic classification, and content categorization.
• Recommender Systems: ML algorithms are commonly used in recommender systems to provide personalized recommendations to users. These systems learn user preferences and behavior from historical data to suggest relevant products, movies, music, or content.

Scaling a machine learning model on a larger data set often compromises its accuracy. Another major drawback of ML is that humans need to manually figure out relevant features for the data based on business knowledge and some statistical analysis. ML algorithms also struggle while performing complex tasks involving high-dimensional data or intricate patterns.  

Deep learning plays an essential role as a separate branch within the Artificial Intelligence (AI) field due to its unique capabilities and advancements. Deep learning is defined as a machine learning technique that teaches the computer to learn from the data that is inspired by humans. DL utilizes deep neural networks with multiple layers to learn hierarchical representations of data. It automatically extracts relevant features and eliminates manual feature engineering. DL can handle complex tasks and large-scale datasets more effectively. Despite the increased complexity and interpretability challenges, DL has shown tremendous success in various domains, including computer vision, natural language processing, and speech recognition. 

 Show me the data ….As impressive as they are at language generation, reasoning, and translation, gen AI applications that have been built on public data can’t realize their full potential in the enterprise until they’re coupled with enterprise data stores. Most organizations store massive amounts of data, both on-premises and in the cloud. Many of these businesses have data science practices that leverage structured data for traditional analytics, such as forecasting. To maximize the value of gen AI, these companies need to open up to the vast world of unstructured and semi structured data as well.
Unlock the full potential of generative AI by implementing these best practices. By leveraging a wide range of data sources, utilizing large language models on unstructured data, and fine-tuning your models for optimal performance, you can gain a competitive edge in today's data-driven business landscape and achieve superior results.

Business intelligence with advanced AI technologies for local & private use automation.  

In today's landscape, Large Language Models (LLMs) have revolutionized AI and business intelligence applications through data training and inference. However, to fully exploit the potential of these models, it's crucial to understand the importance of high-quality data. Here's a comprehensive guide on leveraging three primary types of data sources and fine-tuning your models for optimal performance.

LLMs are also adept at analyzing documents, summarizing unstructured text, and converting unstructured text into structured table formats. For enterprises that figure out how to use this data, it can provide a competitive advantage, especially in the era of gen AI.

1. Diversify your data sources: To maximize the potential of your gen AI endeavors, it's essential to utilize a wide range of data sources: First-party data: Internal data generated through customer and prospect interactions. Second-party data: Data produced in collaboration with trusted partners, such as inventory data shared with e-commerce or retail channels. Third-party data: External data acquired to enrich internal datasets, such as manufacturing supply chain or financial market data.
2. Unleash the power of LLMs on unstructured data: LLMs excel at analyzing documents, summarizing unstructured text, and converting it into structured table formats. By effectively harnessing this data, enterprises can gain a competitive advantage in the era of gen AI.
3. Bridge the gap between AI and enterprise data: While LLMs are impressive in language generation, reasoning, and translation, their full potential in the enterprise can only be realized when coupled with enterprise data stores. Organizations store vast amounts of data, both on-premises and in the cloud. To maximize the value of gen AI, businesses need to embrace not only structured data for traditional analytics but also unstructured and semi-structured data.
4. Fine-tuning the model using reliable and accurate data: To ensure the best performance of your gen AI applications, it's crucial to fine-tune your models using reliable and accurate data. This process involves adjusting model parameters and optimizing algorithms to improve prediction accuracy and reduce bias.

In conclusion, by leveraging a diverse range of data sources, utilizing LLMs' capabilities on unstructured data, and fine-tuning your models with reliable data, you can unlock the full potential of gen AI and gain a competitive edge in today's data-driven business landscape.