Harnessing the Power of GEN AI for Business Transformation

In the pursuit of elevating productivity, maximizing revenue streams, and unlocking unparalleled benefits, integrating Generative AI into your products and across your company is the key to catalyzing a transformative shift. This report explores the strategic implementation of Generative AI, offering insights into its potential to revolutionize workflows, enhance creativity, and drive innovation for sustained business success. 

Before delving into the report, let's explore the evolutionary journey of AI, tracing its development from rule-based systems to the emergence of Generative AI.

The Early Years: Rule-Based Systems and Simple Models - In the nascent years of artificial intelligence, rule-based systems and simple models dominated the landscape. These systems relied on explicit instructions programmed by developers, making decisions based on predefined rules. Imagine a set of if-else conditions guiding a computer's actions—this was the dawn of AI.

1. The Rise of Statistical Learning: To overcome the limitations of rule-based systems, statistical learning emerged. This era focused on allowing machines to learn from data, making decisions based on statistical patterns. 

2. Unveiling the Power of Machine Learning: Machine learning became the cornerstone of AI evolution, with a focus on supervised, unsupervised, and semi-supervised learning. Supervised learning involved using labeled data, unsupervised learning tackled unlabeled data, and semi-supervised learning found a middle ground.

3. Navigating the Landscape of Natural Language Processing (NLP): Fast forward to the era of Natural Language Processing (NLP), where computers aim to understand human language. This required transforming text or large corpus into a format of numerical representations. To bridge this gap, various techniques like one-hot encoding, bag of words, tf-idf, and word embeddings were employed. Enter pretrained models like BERT, GPT and LLAMA, which marked the rise of Generative AI (Gen AI).

4. The Rise of Pretrained Models: A breakthrough occurred with the rise of pretrained models based on transformer architecture. These models, such as GPT, BERT, and LLAMA, were trained on massive datasets, streamlining the process of adapting them for specific use cases. 

5. Foundation Models: Unveiling the Backbone of Gen AI: Foundation models are AI models trained (also known as pre trained models) on vast amounts of unlabeled data, forming the backbone of Gen AI. Two prominent types are Language Model Models (LLM) for Natural Language Processing and diffusion models for Computer Vision.

6. Harnessing Foundation Models: Fine Tuning and Prompting: The beauty of foundation models lies in their adaptability. Fine Tuning and prompting are two ways to harness their power. Fine Tuning involves training a pretrained model on specific datasets, tailoring it for distinct purposes. Prompting, on the other hand, involves providing specific instructions or queries to the model to elicit desired responses.

Have you ever incorporated revolutionary technologies like ChatGPT and MidJourney into your professional and personal endeavors? Undoubtedly, these technologies have made a significant impact and seamlessly integrated into our daily lives. Curious about the mechanics behind their effectiveness? The key lies in the potency of Foundation Models.

Foundation models are the driving force behind the next generation of intelligent machines, empowering them to see, hear, and think in ways that were once only possible for humans

– Demis Hassabis, CEO of DeepMind

What are Foundation Models?

Foundation models are AI models trained on huge amounts of unlabeled datasets that can be used to solve multiple downstream tasks.

As we all know that gen ai is built on pretrained large language models or we can say foundation models. Foundation models are trained on massive datasets and perform a broad set of tasks. Developers use foundation models as the basis for powerful generative AI applications, such as ChatGPT.

For instance, Foundation models trained on the text data can be used to solve problems related to text like Question Answering, Named Entity Recognition, Information Extraction, etc. Similarly, Foundation models trained on images can solve problems related to images like image captioning, object recognition, image search, etc.

There are 5 key characteristics of Foundation Models:

1. Pretrained (using large data and massive compute so that it is ready to be used without any additional training)

2. Generalized — one model for many tasks (unlike traditional AI which was specific for a task such as image recognition)

3. Adaptable (through prompting — the input to the model using say text)

4. Large (in terms of model size and data size e.g. GPT-3 has 175B parameters and was trained on about 500,000 million words, equivalent to over 10 lifetimes of humans reading nonstop!)

5. Self-supervised — no specific labels are provided and the model has to learn from the patterns in the data which is provided. 

What are the Different Foundation Models?

Foundation models are classified into different types based on the domain that they are trained on. Broadly, it can be classified into 2 types.

1. Foundation Models for Natural Language Processing a.k.a Large Language Models

2. Foundation Models for Computer Vision a.k.a Diffusion Models

“Transfer learning is what makes foundation models possible, but scale is what makes them powerful.”

Here is a visual representation featuring a selection of foundation models, along with a timeline illustrating their evolution over time.

Organizations ranging from large corporations, educational research labs, startups, and multinational companies are actively involved in developing foundation models. These models are typically categorized based on licensing into either open source or closed source (proprietary) or third party cloud services like AWS or GCP.

Open source:

Pros: Open source models are easier to customize, provide more transparency into training data, and give users better control over costs, outputs, privacy, and security.

Cons: Open source models may require more work to prepare for deployment and can also require more fine-tuning and training. While set-up costs may be higher with open source models, at scale, companies will have more control over costs versus closed source models where usage can be hard to predict and costs can spiral out of control.

Closed source:

Pros: Closed source models typically provide managed infrastructure and compute environments (e.g. GPT-4²). They may also provide ecosystem extensions that extend model capabilities such as OpenAI’s ChatGPT plugins. Closed source models may also offer more “out of the box” capabilities and value since they are pre-trained and often accessible via an API.

Cons: Closed sourced models are black box, so users get little insight into their training data, making it difficult to explain and tune outputs. Vendor lock-in can also make costs hard to control — for example, usage of GPT-4 is charged on both prompts and completion.

Third Party Cloud Services:

Cloud services from third-party providers typically offer fully managed services, providing access to cutting-edge foundation models from AI companies via an API. Additionally, they include developer tools to facilitate the development and scalability of generative AI applications.

AWS Bedrock 

Amazon Bedrock is a fully managed service from Amazon Web Services (AWS) that offers a variety of Foundation Models (FMs) from leading AI startups and Amazon itself. Users can access these models via an API to facilitate generative AI application development. Users can choose from a selection of FMs based on their specific use case, privately customize these models using their data, and integrate them into their applications using familiar AWS tools and capabilities​​.

The beauty of foundation models lies in their adaptability. Fine Tuning & prompting are two ways to harness their power. 

1. Fine tuning: Fine Tuning involves training a pretrained model on specific datasets, tailoring it for distinct purposes. Fine-tuning customizes pretrained OpenAI models for your specific use case and applications. Effective for bespoke behavior and performance when applying an OpenAI model to new data domains.

1. Prompting: Prompting, on the other hand, involves providing specific instructions or queries to the model to elicit desired responses.

What’s a LLM Hallucination?

LLMs are a type of artificial intelligence (AI) that are trained on massive datasets of text and code. They can generate text, translate languages, write different kinds of creative content, and answer questions in informative ways. However, LLMs are also prone to “hallucinating,” which means that they can generate text that is factually incorrect or nonsensical. Such hallucinations happen because LLMs are trained on data that is often incomplete or contradictory. As a result, they may learn to associate certain words or phrases with certain concepts, even if those associations are not accurate or are unintentionally “overly accurate” (by this I mean they can make up things that are true but not meant to be shared). This can lead to LLMs generating text that is factually incorrect, inadvertently overly indulgent, or simply nonsensical.

Types of hallucinations:

1. Lies: Language models might generate text that is literally untrue and has no factual foundations.

2. Nonsensical: LLMs produce irrelevant or unasked details that don’t correlate to the prompts.

3. Source conflation: The language model attempts to combine information extracted from different sources, resulting in factual contradictions.

Strategies for Avoiding LLM Hallucinations

1. Context Injection & Advanced Prompt Engineering

2. One-shot and few-shots prompting

3. Retrieval-Augmented Generation (RAG)

4. Domain-specific fine-tuning

How RAG work with advanced prompt Engineering:

Understanding Retrieval Augmented Generation (RAG)

A significant concern lies in the occasional presentation of inaccurate or outdated information by these LLMs. Additionally, these models lack the provision of sources for their responses, posing challenges in verifying the reliability of their output. This limitation becomes particularly crucial in contexts where accuracy and traceability are essential. The emergence of Retrieval Augmented Generation (RAG) in AI represents a transformative paradigm, holding the potential to revolutionize the capabilities of LLMs.

Why Do We Need RAG?

RAG was created to tackle the challenges faced by Large Language Models (LLMs) like GPT. Although LLMs can generate text well, they sometimes struggle to give responses that fit the context, limiting their usefulness. RAG is designed to fill this gap by providing a solution that's great at understanding what users mean and giving thoughtful and context-aware answers.

RAG is fundamentally a hybrid model that seamlessly integrates two critical components. 

Retrieval-based methods involve accessing and extracting information from external knowledge sources such as databases, articles, or websites. 

On the other hand, generative models excel in generating coherent and contextually relevant text. What distinguishes RAG is its ability to harmonize these two components, creating a symbiotic relationship that allows it to comprehend user queries deeply and produce responses that are not just accurate but also contextually rich.

Benefits of Retrieval Augmented Generation (RAG)

1. Enhanced LLM Memory

2. Improved Contextualization

3. Updatable Memory

4. Source Citations

5. Reduced Hallucinations

How to build RAG-based Gen AI Applications?

Knowledge graphs and vector databases are the two primary contenders as potential solutions for implementing retrieval augmented generation (RAG).

1. Vector database: A vector database comprises high-dimensional vectors representing entities like words, phrases, or documents. It gauges similarity between entities based on vector representations, indicating relationships. For instance, it can reveal that "Paris" and "France" share a closer relation than "Paris" and "Germany" through vector distances.

2. Knowledge graph: In contrast, a knowledge graph consists of nodes and edges representing entities and relationships, offering facts, properties, or categories. It enables querying or inferring factual details about entities based on node and edge attributes. For instance, a knowledge graph can affirm that "Paris" is the capital of "France" using their edge label.

Why are knowledge graphs better than vector databases? 

1. Answering Complex Questions: The higher the complexity of the question, the harder it is for a vector database to quickly and efficiently return results. Adding more subjects to a query makes it harder for the database to find the information you want.

2. Getting Complete Responses: Vector databases are more likely to provide incomplete or irrelevant results when returning an answer because they rely on similarity scoring and a predefined result limit.

3. Getting Credible Responses: Vector databases can connect two factual pieces of information together and infer something inaccurate.

4. Correcting LLM Hallucinations: Knowledge graphs have a human-readable representation of data, whereas vector databases offer only a black box.

In conclusion, knowledge graphs are a better solution for LLM hallucination than vector databases, because they can provide more precise, specific, complex, diverse, reasoning and inference information to the LLM. This can help the LLM generate text that is more factual, accurate, relevant, diverse, interesting, logical and consistent.

We think with rag we have to experiment with vector db and knowledge graph db with the use case data then only we are able to tell which one will be best for your use case.