Supercharge productivity with Large Language Models (LLMs)

Mastering the ABCs of App Building with Large Language Models, Unveiling the Tech Stack

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By harnessing the power of large language models, develop innovative apps that serve as transformative facades, breathing new life into your existing applications and delivering unparalleled user experiences.

Large Language Models (LLMs) are at the forefront of a linguistic revolution, reshaping the way we interact with technology and information. These advanced AI models, trained on vast amounts of textual data, have the unparalleled ability to understand context, generate coherent content, and assist users in a myriad of tasks. From drafting emails to creating literary pieces, LLMs are empowering creativity by providing tools that augment human capabilities. As we integrate LLM-based apps into our daily lives, we’re not just harnessing the power of AI; we’re redefining the boundaries of human-machine collaboration, setting the stage for a future where language is not just communicated but co-created.

The Building Blocks of Generative AI: A Comprehensive Overview

Generative AI has been making waves in the tech industry, and for a good reason. It has the potential to revolutionize various sectors, from content creation to healthcare. In this article, we’ll delve deep into the building blocks of Generative AI, using insights from Jonathan Shriftman’s article on Medium.

1. Introduction to Generative AI

Generative AI is a subset of artificial intelligence that focuses on creating new content. This could be in the form of text, images, music, or any other form of data. The article emphasizes the rapid advancements in the foundational components of generative solutions. These advancements are not just in terms of technology but also in venture investment.

2. Large Language & Foundational Models

Large Language Models (LLMs) are computer programs trained using vast amounts of text and code. Their primary goal is to understand the meaning of words and phrases and generate new sentences. These models, also known as foundation models, form the basis for various applications. They use vast datasets to learn and, while they might make occasional errors, their efficiency is continually improving.

3. Semiconductors, Chips, and Cloud Hosting

Generative AI models require powerful computational resources. GPUs and TPUs, specialized chips, form the base of the Generative AI infrastructure stack. Cloud platforms like AWS, Microsoft Azure, and Google Cloud provide the necessary resources for training and deploying these models.

4. Orchestration Layer / Application Frameworks

Application frameworks help in the seamless integration of AI models with different data sources. They speed up the prototyping and use of Generative AI models. LangChain and Fixie AI are notable companies in this domain.

5. Vector Databases

Vector databases store data in a way that facilitates finding similar data. They represent each data piece as a vector, a list of numbers corresponding to the data’s features. Pinecone, Chroma, and Weaviate are some of the companies that have developed vector databases.

6. Fine-Tuning

Fine-tuning involves further training a model on a specific task or dataset to enhance its performance. It’s like refining an athlete’s skills for a particular sport. Weights and Bias is a notable company in this field.

Read Choose Wisely Between RAG and Fine-Tuning for Optimal Performance! Insightful article from Towards Data Science

7. Labeling

Data labeling is crucial for generative AI models. It involves providing labels to teach the machine learning model. Snorkel AI and Labelbox are leading companies in this domain.

8. Synthetic Data

Synthetic data is artificially created data that mimics real data. It’s used when real data is unavailable or cannot be used.


Generative AI holds immense potential. Its foundational components are rapidly evolving, and keeping up with these advancements is essential for anyone interested in the field. By understanding its building blocks, we can better appreciate its capabilities and the future it promises.

Note: For a deeper understanding and more insights, you can read the full article by Jonathan Shriftman here.

There are several tools, libraries, and frameworks that can be used to develop Language Model (LLM) applications, enabling you to leverage the power of natural language processing and generation. Here are some popular ones:

These tools and frameworks provide developers with the resources they need to build powerful and efficient LLM applications, enabling tasks ranging from simple text generation to complex natural language understanding and processing tasks.

LangChain: Revolutionizing Multilingual Content Creation and Translation

LangChain is a pioneering platform transforming the landscape of multilingual content creation and translation. With a vision to bridge language barriers and enable seamless communication, LangChain harnesses the power of AI and blockchain technology. This innovative synergy allows users to create, translate, and share content across languages with unprecedented efficiency and accuracy. LangChain’s decentralized approach ensures data security and privacy, while AI-driven algorithms guarantee high-quality translations. Whether you’re a content creator, business owner, or individual seeking to connect with a global audience, LangChain empowers you to break down linguistic barriers and foster meaningful connections in the ever-expanding digital world.

LangSmith: Crafting Language Solutions with AI Precision

LangSmith is a pioneering platform that specializes in harnessing the capabilities of artificial intelligence to offer innovative language solutions. With a commitment to transforming how we interact with and understand languages, LangSmith leverages cutting-edge AI algorithms to provide services like language translation, text generation, and sentiment analysis. Its advanced natural language processing techniques ensure accurate and contextually relevant outcomes, whether it’s converting content between languages or extracting insights from textual data. LangSmith’s dedication to delivering language solutions that exceed expectations makes it a valuable resource for businesses, researchers, and individuals seeking to harness the power of AI for improved language-related tasks.

Semantic Kernel: Empower Your Apps with Seamless AI Integration: Unleash the Potential of Semantic Kernel!

The Semantic Kernel is an open-source software development kit (SDK) that seamlessly merges AI services like OpenAI, Azure OpenAI, and Hugging Face with traditional programming languages like C# and Python. This integration empowers you to craft AI applications that harness the strengths of both domains, resulting in a harmonious synergy of capabilities.

LlamaIndex - Data Framework for LLM Applications

LlamaIndex is a cutting-edge data framework designed to seamlessly connect custom data sources to large language models (LLMs). Its primary objective is to harness the power of LLMs over diverse data sets. The platform offers a range of tools that facilitate data ingestion, allowing users to integrate various data sources and formats, such as APIs, PDFs, documents, and SQL, with large language model applications. Additionally, LlamaIndex provides data indexing capabilities to store and categorize data for different applications, integrating with downstream vector stores and database providers. One of its standout features is the query interface, which accepts any input prompt over the data and delivers a knowledge-augmented response. This framework is instrumental in building robust end-user applications, including Document Q&A for unstructured data, data-augmented chatbots, knowledge agents, and structured analytics using natural language queries.

Get started on LlamaIndex 🦙

Writing Language Model (LLM)-based apps involve leveraging the power of AI to create applications that can understand, generate, and retrieve text-based content. Here’s a step-by-step guide on how to achieve this:

Use Case of Vector Database

A vector database is a specialized type of database designed to handle high-dimensional data, particularly in the form of vectors. These vectors are mathematical representations of data points in a multi-dimensional space. The primary advantage of a vector database is its ability to perform similarity searches, where the goal is to find vectors that are close to a given query vector in the multi-dimensional space.

Example Applications

The utility of vector databases can be best understood through practical applications. Here are two examples that demonstrate how to use sizing guidelines to choose the appropriate type, size, and number of pods for indexing:

Example 1: Semantic Search of News Articles

Example 2: Facial Recognition for Banking Security

Vector databases offer a powerful solution for applications that require similarity searches in high-dimensional spaces. By understanding the data’s size and dimensionality, one can make informed decisions about the type and number of pods required for optimal performance. Whether it’s searching for semantically similar news articles or ensuring secure facial recognition for banking, vector databases provide the necessary infrastructure for efficient and accurate results.

RAG (Retrieval-Augmented Generation)

RAG is an abbreviation for Retrieval-Augmented Generation, a method that combines the strengths of large-scale pre-trained language models with external retrieval or search mechanisms. The primary goal of RAG is to enhance the capabilities of generative models by allowing them to pull relevant information from vast external sources, such as databases or corpora, during the generation process.

In the realm of artificial intelligence, large language models (LLMs) have made significant strides, offering impressive results in various tasks. However, they are not without their flaws. Marina Danilevsky, a Senior Research Scientist at IBM Research, introduces us to a framework designed to enhance the accuracy and relevance of LLMs: Retrieval-Augmented Generation, or RAG.

Watch on YouTube in IBM Technology channel

At its core, the “Generation” in RAG refers to LLMs that produce text in response to a user’s query, known as a prompt. While these models can generate impressive results, they can sometimes provide outdated or unsourced information. For instance, if one were to ask about the planet with the most moons in our solar system, an outdated model might confidently state “Jupiter,” even if newer data suggests otherwise.

The “Retrieval-Augmented” component of RAG aims to address these shortcomings. Instead of solely relying on the LLM’s training data, RAG introduces a content store, which can be an open source like the internet or a closed collection of documents. When a user poses a question, the LLM first consults this content store to retrieve the most relevant and up-to-date information before generating a response.

In practice, this means that when a user prompts the LLM with a question, the model doesn’t immediately respond. Instead, it first retrieves pertinent content, combines it with the user’s query, and then formulates a response. This approach not only ensures that the answer is grounded in current data but also allows the model to provide evidence for its response.

Addressing LLM Challenges with RAG

RAG effectively tackles two primary challenges associated with LLMs:

Outdated Information: Instead of continuously retraining the model with new data, one can simply update the content store with the latest information. This ensures that the model always has access to the most recent data when generating a response.

Lack of Sourcing: By instructing the LLM to consult primary source data before responding, RAG reduces the likelihood of the model providing unsourced or fabricated answers. This approach also enables the model to recognize when it doesn’t have enough information to answer a query, allowing it to respond with “I don’t know” rather than potentially misleading the user.

However, the effectiveness of RAG is contingent on the quality of the retriever. If the retriever fails to provide high-quality grounding information, the LLM might not be able to answer a user’s query, even if it’s answerable.

Components of RAG:

How RAG Works:

Advantages of RAG:

Applications of RAG:

RAG represents a significant step forward in the realm of generative models by bridging the gap between retrieval-based and generation-based approaches. By combining the strengths of both methods, RAG offers a powerful tool for a wide range of natural language processing tasks, especially those that benefit from external knowledge retrieval.

By combining the capabilities of Language Models, vector databases, and RAG techniques, you can create powerful applications that provide contextually relevant and engaging text-based interactions for users.

Retrieval-Augmented Generation offers a promising solution to some of the challenges faced by large language models. By grounding responses in up-to-date, sourced information, RAG ensures that users receive accurate and trustworthy answers to their queries. As research in this area continues, we can expect even more refined and effective implementations of this framework in the future.

Further Reference

If you are interested in Citizen Development, refer to this book outline here on Empower Innovation: A Guide to Citizen Development in Microsoft 365

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If you wish to delve into GenAI, read Enter the world of Generative AI

Also, you can look at this blog post series from various sources.

  • Hackernoon
  • Hashnode
  • Medium
  • Stay tuned! on Generative AI Blog Series

    Certain part of this post was generated through web-scraping techniques using tools like Scrapy and Beautiful Soup. The content was then processed, summarized, and enhanced using the OpenAI API and WebPilot tool. We ensure that all content undergoes a thorough review for accuracy and correctness before publication