How To Start Your Journey in Generative AI (LLM) in 2024: Step-by-Step Guide

Generative AI roadmap

Over the past year, there has been a significant surge in the utilization of Large Language Models (LLMs) in Generative AI. If you find yourself overwhelmed by the abundance of scattered resources available online, consider the following structured approach as your foundational path.

You can refer this video as well to understand the roadmap:

Here are seven fundamental steps to guide you:

1. NLP Fundamentals

Understand the basics of Natural Language Processing (NLP), including concepts such as embeddings and vector spaces.

These fundamentals will help you to build your core skills required to understand LLM. I know when I say “fundamentals”, you might get confused with “what topics to cover exactly?” I tell you, most important is, understanding embeddings. Yes, All types of embeddings. As core idea of any development in NLP, is to make the embeddings better so that your program/algorithm/ML/NN will be able to understand the context better close to a human.

You Can Learn: Frequency-based embeddings, Prediction-based embeddings, Seq2seq Models, especially Transformers.

2. LLM Models and their architecture

Delving into the architecture and pre-training methodologies of Large Language Models (LLMs) is essential for understanding their capabilities and leveraging their power effectively. Familiarizing yourself with different models such as Falcon, LLAMA, Gemini, Mistral, Alpaca, and others provides insights into their key differences, enabling you to choose the most suitable one for your use case.

Each LLM model comes with its own architecture and pre-training approach, tailored to address specific challenges and objectives. Understanding these architectures allows you to distinguish LLMs from existing AI models and appreciate their unique capabilities. Moreover, as new LLM models continue to emerge at a rapid pace, having a grasp of their architectures facilitates quick comprehension and evaluation.

Here’s a brief overview of some popular LLM models and their distinguishing features:

1. Falcon: Known for its efficient architecture and scalability, Falcon emphasizes speed and performance, making it suitable for applications requiring real-time or low-latency responses.

2. LLAMA: LLAMA focuses on multimodal learning, combining text with other modalities such as images or audio. This enables the model to understand and generate responses based on a wider range of inputs.

3. Gemini: Gemini is designed for collaborative and conversational tasks, with an architecture optimized for interactive dialogue systems and multi-turn conversations.

4. Mistral: Mistral emphasizes fine-grained control over generated outputs, allowing users to specify constraints and preferences to tailor the responses to their needs more precisely.

5. Alpaca: Alpaca stands out for its attention to ethical considerations, with built-in mechanisms to detect and mitigate biases in generated text, making it suitable for applications requiring fairness and accountability.

By understanding the architectures and pre-training methodologies of these LLM models, you can effectively assess their strengths, weaknesses, and suitability for various use cases. This knowledge not only enables you to make informed decisions when choosing a model but also empowers you to adapt and leverage LLM technology to its fullest potential as new models continue to evolve.

3. LLM Fine-Tuning

Fine-tuning Large Language Models (LLMs) for specific tasks or domains is a crucial process to adapt these generalized models to particular use cases or datasets. Since LLMs are trained on vast amounts of data, they tend to offer broad and generalized knowledge. However, for tasks requiring specialized outputs or incorporating new information, fine-tuning is necessary.

The fine-tuning process involves retraining the pre-trained LLM on a smaller, domain-specific dataset or with task-specific examples. By doing so, the model learns to adjust its parameters to better understand and generate outputs relevant to the target domain or task.

While fine-tuning LLMs is effective, it can also be resource-intensive and time-consuming, particularly when dealing with large models and datasets. To address this challenge, an alternative technique called Retrieval Augmented Generation (RAG) has been proposed.

4. Retrieval Augmented Generation (RAG):

Retrieval Augmented Generation (RAG) is a powerful technique used to enhance the capabilities of Large Language Models (LLMs) by incorporating additional information sources into the model’s ecosystem. This approach is particularly useful when specific datasets are required for generating outputs or when the knowledge within the LLM becomes outdated and needs updating with new information.

In RAG architecture, an additional information source, often a retrieval-based system or a knowledge base, is integrated with the LLM. When a user query falls outside the scope of the LLM or requires access to specific datasets, the RAG system extracts relevant information from this additional source. This extracted information is then added to the user query as an instruction, enriching the context provided to the LLM.

Subsequently, the augmented query, now containing both the user input and the retrieved information, is fed into the LLM. By incorporating this supplementary data, the LLM gains access to a broader knowledge base, enabling it to generate more informed and contextually relevant responses.

In today’s diverse range of use cases, RAG architecture has become increasingly essential for obtaining efficient and accurate responses from LLM models. By seamlessly integrating retrieval-based systems or knowledge bases with LLMs, organizations can leverage the combined power of these technologies to meet the complex demands of modern applications and user queries effectively.

Overall, while fine-tuning remains a valuable technique for task-specific adaptation of LLMs, RAG offers an efficient alternative that leverages external knowledge sources to enhance the model’s performance and responsiveness to specific tasks or domains. Both approaches play crucial roles in enabling LLMs to meet the diverse needs of various applications and use cases effectively.

5. Prompt Engineering and Evaluation

Prompt engineering is the practice of crafting effective prompts or queries for Large Language Models (LLMs) to guide them in generating relevant and high-quality responses. It involves structuring user queries in a well-organized manner to provide context and improve the model’s understanding, thereby enhancing the quality of generated outputs.

Unlike traditional user queries, prompts are designed to be reusable and standardized, allowing users to input queries in a consistent format. This ensures that if a query is repeated, the same prompt can be used without needing to rewrite it each time. Additionally, prompts help provide context to the LLM, enabling it to better understand the user’s intent and generate more accurate responses.

In prompt engineering, the goal is to design prompts that effectively communicate the desired task or information to the LLM while minimizing ambiguity and maximizing relevance. This may involve using specific keywords, formatting guidelines, or providing additional context to clarify the user’s intent.

In addition to prompt engineering, evaluating the performance of LLMs is essential for assessing their effectiveness and identifying areas for improvement. Evaluation techniques for LLMs differ from those used in traditional AI systems and may include:

1. Human Feedback: Gathering feedback from human evaluators to assess the quality, relevance, and coherence of the LLM’s generated responses.

2. Perplexity: Measuring the uncertainty or “surprise” of the LLM’s predictions based on its language modeling capabilities. A lower perplexity indicates better model performance.

3. BLEU Score: Calculating the similarity between the LLM’s generated responses and reference responses provided by human evaluators. The BLEU score quantifies the quality of machine-generated text based on n-gram overlap.

By mastering prompt engineering and understanding evaluation techniques tailored for LLMs, developers can fine-tune their models, improve their performance, and ensure they meet the desired quality standards. These practices play a crucial role in enhancing the usability and effectiveness of LLM-based applications across various domains.

6. LLM Frameworks and Tools

Familiarizing yourself with frameworks and tools used in developing and deploying Large Language Model (LLM)–based applications is crucial for building robust and efficient systems. These frameworks provide a structured environment and pre-defined functionalities that streamline the development process and ensure seamless deployment.

An LLM framework or orchestration tool serves as the backbone of your development process, facilitating the integration of various components and ensuring a systematic flow from development to deployment.

For instance, LangChain stands out as a popular framework tailored for working with LLMs. Understanding such frameworks in depth is essential for building end-to-end applications that leverage the power of LLMs effectively.

7. LLM Deployment and LLMOps

Now that you’ve covered the basics of Large Language Models (LLMs), including their components, architecture, and even built your own use case using a framework, it’s time to focus on deploying your model for user accessibility.

Deploying LLM-based systems typically involves making them available through Application Programming Interfaces (APIs) or leveraging cloud hosting services. This allows users to interact with the model without needing to understand its internal workings or complexities.

API deployment involves exposing the functionality of your LLM as endpoints that users can access over the internet. Users can send requests to these endpoints, and the model processes the input data and returns the desired output. APIs are commonly used for integrating LLMs into web applications, mobile apps, or other software systems.

Cloud hosting services provide a convenient way to deploy and manage LLM-based systems without the need to set up and maintain infrastructure yourself. Platforms like Amazon Web Services (AWS), Google Cloud Platform (GCP), and Microsoft Azure offer services specifically designed for deploying machine learning models, such as AWS Lambda, Google Cloud Functions, or Azure Functions. These services handle the deployment, scaling, and monitoring of your LLM, allowing you to focus on building and improving your application.

By deploying your LLM through APIs or cloud hosting services, you make it accessible to a wider audience, enabling users to leverage its capabilities without having to worry about the underlying technical details. This approach also facilitates integration with existing systems and workflows, making it easier to incorporate LLM-based functionality into various applications and use cases.


Once you’ve mastered these fundamental concepts and followed the outlined path, navigating and comprehending related tools and advancements will become more accessible. These basics will serve as the cornerstone for understanding further developments.

In the upcoming blog post, I’ll delve into the practical implementation of these seven fundamentals in real-world projects by discussing the relevant tools. Stay tuned for more updates!

If you find this blog helpful, please consider giving it a like, and feel free to ask any questions or suggest topics for further explanation in the comments section. Your feedback and input are valuable in shaping the content of future blog posts related to LLM

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