Build Applications with Generative AI and LLMs

A guide for developers who want to leverage the power of generative AI and LLMs for their use cases

Introduction

Generative AI is a branch of artificial intelligence that focuses on creating new content, such as text, images, audio, or video, based on some input or data. LLMs, or large language models, are a type of generative AI that can generate natural language text based on a prompt or a context. LLMs have been trained on massive amounts of text data from various sources and domains and can produce coherent and diverse texts for various tasks and applications.

However, building applications with generative AI and LLMs is not a trivial task. It requires careful consideration of the use case, the data, the development skills, and the technology roadmap. In this document, we will summarize the main points that the user has raised in their request and provide some tips and best practices for building applications with Generative AI and LLMs.

Is Generative AI the Only Solution?

The first question that the user asks is whether Generative AI is the only solution to their use case. This is a very important question, as not every problem needs Generative AI as a solution. Sometimes, a simple API and an application is more than enough to achieve the desired outcome. For example, if the use case is to generate a summary of a news article, a simple API that uses extractive summarization techniques might be sufficient, rather than using a Generative AI model that might introduce errors or biases.

Therefore, before deciding to use Generative AI and LLMs, the user should do a thorough analysis of their use case and compare the pros and cons of different solutions. Some of the factors that the user should consider are:

The complexity and novelty of the use case. If the use case is very complex or novel and requires generating new content that is not easily available or accessible, then Generative AI and LLMs might be a good option. For example, if the use case is to generate a product description based on some keywords or features, then Generative AI and LLMs might be able to produce more creative and engaging texts than a simple template-based approach.

The quality and quantity of the data. If the use case requires generating high-quality and diverse content, then Generative AI and LLMs might be a good option. However, Generative AI and LLMs also require a lot of data to train and fine-tune, and the data should be relevant, reliable, and representative of the target domain and audience. For example, if the use case is to generate a blog post on a specific topic, then Generative AI and LLMs might be able to produce more informative and relevant texts than a simple keyword-based approach. However, the user should also have access to a large and diverse corpus of blog posts on that topic, and ensure that the data is free of errors, plagiarism, or inappropriate content.

The cost and time of the development. If the use case requires generating content quickly and cheaply, then Generative AI and LLMs might not be a good option. Generative AI and LLMs are computationally expensive and time-consuming to train and fine-tune and might require specialized hardware and software resources. For example, if the use case is to generate a short email reply to a customer query, then generative AI and LLMs might not be worth the effort and expense, compared to a simple rule-based or template-based approach.

Do You Have Your Data Ready?

The second question that the user asks is whether they have their data ready. This is another crucial question, as data is the fuel for Generative AI and LLMs. Without data, Generative AI and LLMs cannot learn or generate anything. However, having data is not enough. The user should also ensure that their data is private, protected, and has proper access in place. Otherwise, unauthorized access may harm their internal experience or expose their sensitive information. Not everyone in an organization needs to see every data.

Therefore, before using Generative AI and LLMs, the user should do a careful data preparation and management. Some of the steps that the user should follow are:

Data collection. The user should collect data from reliable and relevant sources and ensure that the data is sufficient and suitable for their use case. The user should also check the quality and diversity of the data, and remove any duplicates, outliers, or noise. The user should also respect the copyright and privacy of the data sources and obtain the necessary permissions and consent before using the data.

Data annotation. The user should annotate the data with the appropriate labels, tags, or metadata, depending on the task and the model. The user should also ensure that the data annotation is consistent, accurate, and comprehensive, and follow the best practices and standards for data annotation. The user should also use tools and platforms that facilitate and automate the data annotation process and allow for quality control and verification.

Data storage. The user should store the data in a secure and accessible location and ensure that the data is backed up and encrypted. The user should also use tools and platforms that enable and simplify the data storage and retrieval and allow for data versioning and tracking. The user should also follow the data governance and compliance policies and regulations of their organization and jurisdiction and ensure that the data is not misused or leaked.

Data access. The user should grant data access to the authorized and relevant parties and ensure that the data access is controlled and monitored. The user should also use tools and platforms that support and streamline the data access and sharing and allow for data auditing and logging. The user should also follow the data ethics and responsibility principles and guidelines and ensure that the data is not abused or exploited.

Do You Have the Development Skills?

The third question that the user asks is whether they have the development skills in their organization to take up the project. This is also an important question, as building applications with Generative AI and LLMs requires a combination of technical and domain expertise, as well as creativity and problem-solving skills. The user should not underestimate the challenges and risks involved in developing applications with generative AI and LLMs and should have a realistic and feasible plan and timeline.

Therefore, before starting the development, the user should do a skill assessment and gap analysis and identify the strengths and weaknesses of their team. The user should also do a minimum viable product (MVP) that would validate the understanding and skills of the team and provide feedback and insights for improvement. The user should avoid doing a proof of concept (POC) that might not reflect the real-world scenarios and expectations. The user should also seek external help and guidance from experts and mentors and leverage the available resources and communities for learning and support.

Some of the skills and competencies that the user and their team should have or acquire are:

Generative AI and LLMs fundamentals. The user and their team should have a solid understanding of the concepts and principles of generative AI and LLMs, such as how they work, what they can and cannot do, what are their advantages and limitations, and what are their best practices and pitfalls. The user and their team should also be familiar with the different types and architectures of Generative AI and LLMs, such as autoregressive, autoencoder, variational, adversarial, transformer, GPT, BERT, etc., and their pros and cons for different tasks and applications.

Generative AI and LLMs tools and platforms. The user and their team should have a good command of the tools and platforms that enable and facilitate the development of applications with Generative AI and LLMs, such as programming languages, frameworks, libraries, APIs, IDEs, etc. The user and their team should also be able to use the tools and platforms effectively and efficiently, such as how to install, configure, run, debug, test, deploy, monitor, etc., and how to optimize the performance, scalability, reliability, security, etc.

Generative AI and LLMs applications and use cases. The user and their team should have a clear vision and goal of the applications and use cases that they want to build with Generative AI and LLMs, and how they will benefit their customers and stakeholders. The user and their team should also have a deep knowledge and insight of the domain and the audience of the applications and use cases, and how to tailor and customize the generative AI and LLMs models and outputs to their needs and preferences. The user and their team should also have a creative and innovative mindset and be able to generate novel and valuable ideas and solutions with generative AI and LLMs.

Did You Decide the Right LLM?

The fourth question that the user asks is whether they have decided the right LLM for their project. This is also a critical question, as choosing the right LLM can make a huge difference in the quality and outcome of the applications and use cases. The user should not blindly follow the hype or popularity of a certain LLM, but rather do a careful evaluation and comparison of the different LLMs available and select the one that best suits their requirements and expectations.

Therefore, before choosing an LLM, the user should do a thorough research and analysis of the different LLMs, and consider the following factors:

The size and scope of the LLM. The user should consider the size and scope of the LLM, such as how many parameters, layers, tokens, vocabularies, etc., it has, and how much data, domains, languages, etc., it covers. The user should also consider the trade-offs between the size and scope of the LLM, such as how they affect the accuracy, diversity, generality, specificity, etc., of the LLM outputs. The user should choose an LLM that has an optimal balance between the size and scope, and that matches the complexity and novelty of their use case.

The quality and reliability of the LLM. The user should consider the quality and reliability of the LLM, such as how well it performs on various metrics, benchmarks, tasks, and applications, and how consistent and robust it is across different inputs, outputs, domains, languages, etc. The user should also consider the potential issues and risks of the LLM, such as how prone it is to errors, biases, inconsistencies, or inappropriate content, and how easy or difficult it is to detect and correct them. The user should choose an LLM that has a high quality and reliability, and that meets the standards and expectations of their use case.

The availability and accessibility of the LLM. The user should consider the availability and accessibility of the LLM, such as how easy or difficult it is to obtain, use, modify, or extend the LLM, and what are the costs and resources involved. The user should also consider the compatibility and interoperability of the LLM, such as how well it works with the tools and platforms that they use, and how flexible and adaptable it is to their use case. The user should choose an LLM that has a high availability and accessibility, and that fits their development skills and environment.

The roadmap and future of the LLM. The user should consider the roadmap and future of the LLM, such as how frequently and regularly it is updated, improved, or expanded, and what are the new features, capabilities, or domains that it will support or add. The user should also consider the longevity and sustainability of the LLM, such as how long it will remain relevant, competitive, or supported, and what are the challenges or opportunities that it will face or create. The user should choose an LLM that has a clear and promising roadmap and future, and that aligns with their vision and goal.

Conclusion

In this article, we have summarized the main points that the user has raised in their request and provided some tips and best practices for building applications with Generative AI and LLMs. We hope that this will help the user and their team to make informed and confident decisions, and to create successful and satisfying applications and use cases with Generative AI and LLMs.?

(Thanks to M365 Copilot for editing it and Microsoft Designer to create the headpiece)