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5 stages of Generative AI project lifecycle

There are five distinctive phases of a generative AI project, beginning with constructing a powerful language model and concluding with its seamless integration into real-life scenarios. Whether you're an aspiring writer intrigued by the possibilities of AI in crafting captivating narratives or an entrepreneur seeking innovative solutions to enhance customer engagement, this read is tailor-made for you!

1. Pre-training: Building a Large Language Model from Scratch

This involves building an LLM from scratch. The likes of BERT, GPT4, Llama 2, have undergone pre-training on a large corpus of data. Billions of parameters are trained. Pre-training is an Unsupervised Learning task and the objective is text generation or next token prediction. Pre-training is a compute intensive phase and the training phase lasts for days and even months. The task is complex and everything from the training corpus to the transformer architecturedecided in the pre-training phase. The result of pre-training are Foundation Models

Generative AI project Lifecycle

2. Prompt Engineering: Generating Text through Inferencing

Once the foundation model is ready, text can be generated by providing the model with a prompt. The model generates a completion on the prompt. This process is called inference. No training happens during prompt engineering. None of the model weights are touched. The only examples given are in-context. Prompt engineering is the simplest of phases in the LLM lifecycle. The objective of prompt engineering is to improve performance on the generated text.

3. Fine-tuning: Training the Model for Desired Tasks

Probably, the most important phase of an llm lifecycle is when it is trained to perform well on certain desired tasks. This is done by providing examples of prompts and completions to the foundation model. Fine-tuning is a Supervised Learning task. A complete fine-tuning requires as much memory as pre-training a foundation model. The weights of the foundation model are updated in fine-tuning. PEFT or Parameter Efficient Fine Tuning, reduces the memory requirement of fine-tuning while maintaining performance levels.

4. Reinforcement Learning: Learning from Human/AI Feedback

RLHF or RLAIF proved to be the turning point in acceptance of LLMs. The primary objective of RLH/AIF is to align the llm to the human values of Helpfulness, Harmlessness and Honesty . This is done using rewards. The rewards are initially given by a human in RLHF and then a rewards model is generated. Applying the principles of constitutional AI, RLAIF is used to scale human feedback. The result is a model that is aligned to human values.

5. Compression, Optimization, and Deployment: Making the Model Ready for Application

The final stage is where the LLM is ready to be used in an application. In this stage, the model is optimised for faster inference and lesser memory. Sometimes, a smaller llm derived from the original llm is used in production.

And there you have it - the five stages of the generative AI project lifecycle. From building a large language model to making it ready for application, each stage plays a crucial role in harnessing the power of AI for content marketing. By leveraging this AI-powered content marketing platform, content marketers can create and distribute content that is not only aligned with their brand but also tailored to their target audience's preferences. So, why wait? Start exploring the possibilities of generative AI and take your content marketing to the next level!

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