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 an LLM 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
2. Prompt Engineering: Text Generation 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 Specific 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: With 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. Model Compression, Optimization, & Deployment: Application Ready
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!
FAQs
Q: What are the 5 stages of a generative AI project lifecycle?
A: The five stages are: (1) Pre-training - building the foundation model, (2) Prompt Engineering - generating text through inference, (3) Fine-tuning - training for specific tasks, (4) Reinforcement Learning - aligning with human feedback, and (5) Model Compression & Deployment - optimizing for production use.
Q: What happens during the pre-training phase of an LLM?
A: Pre-training builds an LLM from scratch by training billions of parameters on large datasets. It's an unsupervised learning task focused on text generation and next token prediction. This compute-intensive phase can last days or months and produces foundation models like GPT-4, BERT, and Llama 2.
Q: How is prompt engineering different from model training?
A: Prompt engineering doesn't involve any training—no model weights are updated. You simply provide prompts to an already-trained foundation model to generate completions. It's the simplest phase, using only in-context examples to improve the quality of generated text.
Q: What is fine-tuning and why is it important?
A: Fine-tuning trains a foundation model to perform well on specific desired tasks using prompt-completion examples. It's a supervised learning task that updates the model's weights. This is considered the most important phase as it customizes the general-purpose model for your particular use case.
Q: What is PEFT and how does it help with fine-tuning?
A: PEFT (Parameter Efficient Fine Tuning) reduces the memory requirements of fine-tuning while maintaining performance. Standard fine-tuning requires as much memory as pre-training, making PEFT crucial for organizations with limited computational resources.
Q: What does RLHF mean in AI development?
A: RLHF (Reinforcement Learning with Human Feedback) aligns LLMs to human values of Helpfulness, Harmlessness, and Honesty using reward-based training. Humans initially provide rewards, then a rewards model is generated. RLAIF (with AI Feedback) scales this process using constitutional AI principles.
Q: Why was RLHF a turning point for LLM acceptance?
A: RLHF made LLMs more reliable and trustworthy by aligning them with human values and preferences. This alignment addressed concerns about AI-generated content being harmful, unhelpful, or dishonest—making LLMs suitable for real-world applications.
Q: What happens during model deployment and optimization?
A: The deployment stage optimizes the LLM for faster inference and reduced memory usage. Sometimes a smaller, derived model is used in production instead of the full-sized version to improve performance and reduce costs in real applications.
Q: Do I need to go through all 5 stages to use generative AI?
A: No. Most users start at the prompt engineering stage using pre-trained foundation models. Fine-tuning and RLHF are only needed if you require task-specific customization. Pre-training is reserved for organizations building models from scratch.
Q: How long does each stage of the AI lifecycle take?
A: Pre-training takes days to months and is extremely compute-intensive. Fine-tuning varies based on dataset size and customization needs. Prompt engineering is immediate, while RLHF depends on feedback collection. Deployment timing depends on optimization requirements and infrastructure setup.
