Generative AI models like GPT-4, Bard, and LLaMA are becoming increasingly sought after due to their impressive abilities in various tasks, like text generation, summarization, and categorization. Although big tech firms can develop and sustain the most potent models, smaller firms and companies may have to look at cost-effective methods of Customized Model Development and adapt existing models to meet particular needs.

It is a process of choosing the appropriate model, and making it customizable could be an arduous task and particularly challenging for businesses that are just beginning to learn about this area. Consider things like your intended use, the available computing power and task complexity, data size, quality, scalability, and the expertise required to implement your plan successfully. For example, when deciding on the proper method of customization and model,

In this blog, we share the insights gained from working with growing software firms and testing generative AI techniques and models. In particular, we’ll review the various kinds of Generative AI models and present four ways to tailor the models to an organization’s specific requirements. From fine-tuning to prompt engineering and optimization, reinforcement learning is based on human feedback (RLHF). We will discuss the aspects to consider when deciding on the best method for implementing GenAI models for specific scenarios.

What Is Generative AI?

Generative AI is a kind of AI technology that creates fresh content, including images, text music, text, or other media. It can learn from vast amounts of content already in use. Then, it uses patterns learned from these data to produce new, unique outputs that are comparable but not the same as the content it learned from. Amid generative AI, Machine Learning Prediction Services, including neural networks can train these models by feeding them vast volumes of information.

Their large size, complexity, and extensive learning on massive datasets set generative AI models apart from the previous deep learning models. They demonstrate advanced capabilities, like creating new content through instructions, engaging in logical reasoning, solving mathematical issues, and passing test-like human tests that were impossible in earlier generations of AI models.

Top Ways To Customize Your Models 

In the past, established companies could access the information and intellectual property required to train large-scale base models. However, many small or medium-sized startups typically lack the capabilities (such as experience, expertise, computing capacity, or money) to develop a GPT-like model from scratch. To address this issue, we present four possible ways to customize pre-trained models to help startups achieve differentiation. It also provides a personalized customer experience without the stress of creating huge models from scratch.


Fine-tuning is an approach that involves changing the model’s parameters using additional labeled data to help adapt an all-purpose model to a specific purpose. The refined model keeps the knowledge gained from the pre-training phase while improving in the field. The downside is that fine-tuning can lead to the possibility of overfitting. In this case, the model gets too focused on a specific area and loses its capacity to be effective in other activities.

One recent Model Selection And Tuning variant, mainly used for LLMs, is instruction tuning. Instruction tuning helps refine a previously trained model of language based on a mix of activities formulated into instructions. Improving the model’s performance on certain tasks requiring complex instructions is possible.

Prompt Engineering

Prompt engineering provides a solution for fine-tuning language models to perform specific jobs. The idea was first demonstrated in GPT-3, which proved that frozen models can be trained to accomplish different tasks by “in-context” learning.

People use prompt engineering to prepare the model for a specific task by creating a prompt text with a summary (zero-shot) or job examples (few-shot). To prepare an algorithm for sentiment analysis, ask, “Is the following movie review positive or negative?” An input sequence follows this, “This movie was amazing!” Researchers discovered that particular prompt engineering techniques, like prompting with a few shots or chain-of-thought prompting, could significantly improve the quality of output without the need to fine-tune.

This technique makes it easier to serve a model to several downstream activities by using a single trained language model distributed across multiple projects. Instead of fine-tuning, it removes the requirement to save and provide different models for each task downstream. However, as these research studies and papers suggest, they could have some negatives. Text prompts require manual effort to create, and their effectiveness is often lower than fine-tuned models. For example, a GPT-3 model frozen with 175 billion parameters is 5 points lower in the SuperGLUE benchmark than a finely tuned T5 model with 800 times the number of parameters.

Optimizing The Speed Of Tuning Or Prompt-Tuning

Researchers quickly realized that they could optimize LLM performance to meet specific requirements. Going from the concept of prompt engineering (manually designed prompts) and considering prompts as variables that can be changed. The prompt-tuning method is a compromise between prompt engineering and fine-tuning. It simply tunes the prompt parameters to an exact task without changing the model or adjusting the other parameters. This technique saves energy compared to fine-tuning, produces superior results than prompt engineering, and eliminates the requirement for manual work in creating prompts.

T5-large tuning the prompts involves changing about 50,000 parameters instead of the 32-770 million parameters adjusted in the fine-tuning process. In particular, the automatically-induced prompts can be further separated into two groups: discrete or complex prompts, where the prompt is an actual text string, and continuous or soft prompts. Instead, the prompt is described directly in the embedding space of the underlying LLM.

Discrete, sometimes challenging, prompts are characterized by automatically seeking out templates in an undefined space. They are usually related to the natural language. Like hand-crafted prompts, soft prompts can be combined with input text. Instead of relying on traditional vocabulary tokens, the soft prompts comprise learnable vectors that can be optimized end-to-end over training data. Even though soft prompts aren’t instantly interpretable, they can find evidence of the best way to complete a task by analyzing labeled datasets. Prompt tuning was initially developed for a vast language model and has been extended to different domains like audio and video because of its effectiveness. Prompts may be text-based snippets, voice streams, or pixel blocks within video or still images.

Human Feedback Reinforces Reinforcement Learning (RLHF)

Reinforcement learning from human Feedback (RLHF) can be described as improving language models by aligning them to the human value system, which is not easy to quantify (e.g., humorous, hilarious, helpful, funny). These techniques can assist models in avoiding incorrect answers and reduce the possibility of hallucinations and bias.

The procedure consists of three significant steps:

Pre-Training a Language Model (LM)

This process requires using an existing language model as the basis. This model is pre-trained and educated on a vast textual data corpus and can generate texts based on instructions. There are miniature versions of GPT-3 and DeepMind’s Gopher. The selection of the first design depends on the use case and has not yet been established as a standard.

Gathering Data And Training a Reward Model (RM)

At this point, the feedback from humans is gathered to make a database that assigns a scalar value to the content according to the preferences of humans. Questions are then passed over to the language model used to create new texts, which human annotators evaluate. Various ranking strategies are used to transform the results of human annotators into a scalar reward indicator to help train. The model of reward aims to show human preferences numerically and is utilized to aid in the refinement of the model during the subsequent phase.

The Fine-Tuning Of Your Language Model With Reinforcement Learning (RL)

The final stage is optimizing the language model initially created using reinforcement learning. The goal is to update the parameters to improve the reward metrics derived from the reward model. The process of fine-tuning involves formulating the problem as a Reinforcement Learning (RL) task and defining the policy action space, the action space, the reward function, and the action space. After that, you can use the RL method to enhance the model on the components and for Predictive Analytics.

In the end, RLHF is an exciting and challenging research area that aims to improve language models through human feedback. This method has succeeded in models such as GPT-4 and ChatGPT; however, it requires more research to improve the design environment and comprehend its full application scope.


While technology develops, the possibilities of its applications grow ever more significantly. From research to the expression of artistic talent, Generative AI could revolutionize various sectors. In this article, we’ve covered a variety of artificial intelligence models and four ways to tailor them to specific needs. AI models and four methods to modify them according to the particular application. It is essential to grasp the options available and select the appropriate models that best suit your situation’s needs. Further model customization using suitable techniques and data relevant to the problem is needed.