Navigating the Lifecycle of Generative AI: A Reference Guide

14 min readApr 9, 2024

In the fast-moving field of Generative AI, what’s revolutionary today could quickly become yesterday’s news. Still, this article’s goal is to get you up to speed with the latest in Generative AI, breaking it down into chunkable steps of its lifecycle, and making it a bit easier to grasp for everyone, including myself. Check out this diagram that maps out all the parts of its development steps.

Identifying Use Cases: The first step in the development process is to define what we aim to achieve with our AI. It’s about matching our goals with the right technology, and at the end of the day, this determines the service our app will provide.

Selecting a Suitable Model: Once our goals are set, we delve into selecting the most appropriate AI model for the job. This is a critical decision, as each model has its unique strengths and is designed for different tasks.

Fine-Tuning the Model: After choosing our model, we enter the fine-tuning phase, where we adjust and refine the model’s parameters to best meet our project’s specific requirements. It’s akin to tailoring a suit to fit perfectly.

Evaluating Metrics: Before full deployment, the model undergoes testing to ensure that it meets quality and performance standards. This step verifies the model’s readiness for real-world application.

Deployment: The final step is deploying the AI model, ready to interact with users and make an impact. At this step, making external resources available augments the power of the model.

Use Cases We Can Think of

Text Generation involves AI crafting text from epic tales to snappy chatbot responses, akin to having Shakespeare rolled into one digital entity.
Text Summarization aims to distill lengthy texts into bite-sized summaries, capturing the essence of documents.
Language Translation breaks down the Tower of Babel, easily bridging languages and cultures, and conveying the same humor, emotion, and vibe across languages.
Information Retrieval sifts through the digital stacks to fetch what you need. It’s like having a search engine with intuition.
Large Language Models (LLMs) harmonize web services and APIs to perform complex tasks seamlessly.
Sentiment Analysis gauges emotions from text to understand public opinion, customer feedback, or social media vibes, offering insights into the collective mood.

Under the Hood

These are underlying techniques used to make the LLMs work. Let’s find out what they are.

Tokenizing

First things first, as every master chef knows the importance of prep, the LLMs start with tokenizing. Imagine taking a whole block of text and slicing it into bite-sized pieces, including words, phrases, or even individual characters. This step is to make the text manageable and ready for what comes next.

Embedding

This is the process of converting tokens into vectors of real numbers that the LLM can work with. This enables it to understand and generate language based on the semantic and syntactic similarities encoded in these vectors. It’s akin to marinating ingredients in a special sauce.

Transformers

At the heart of the LLMs is the transformer, and it relies on a mechanism called self-attention to weigh the importance of different words in a sentence, regardless of their positional distance from each other. This is a significant shift from previous sequence processing models, such as Recurrent Neural Networks (RNNs) or Convolutional Neural Networks (CNNs). This process ensures that every word is considered in context, blending flavors to create a cohesive whole.

Encoder and Decoder

Nestled within the transformer are the encoder and the decoder. They work together to handle sequence-to-sequence tasks, such as translation from one language to another. Unlike RNNs, which process the data sequentially, the encoder reads and processes the entire input sequence simultaneously. Conversely, the decoder is designed to generate the output sequence from the encoded representation provided by the encoder.

Self-Attention and Multi-Headed Self-Attention

Self-attention allows each position in a sequence to attend to all positions within the same sequence. This enables the LLM to consider the full context of the sequence, capturing relationships and dependencies between elements to understand the context and meaning of the text.

Multi-headed self-attention extends the self-attention mechanism by parallelizing the attention process. Each head can learn to attend to different aspects of the input data, such as various types of syntactic and semantic relationships. This leads to a richer and more nuanced understanding of the sequence.

Feed Forward Networks

A Feed Forward Network is integral to each layer of both the encoder and the decoder. This network transforms the output of the self-attention mechanism using learned weights and non-linear activation functions, thereby capturing complex patterns in language processing tasks.

Model Selection

Autoencoding Models, Autoregressive Models, and Sequence-to-Sequence Models are the core algorithms that understand, predict, and translate our world of words. Let’s break down how each of these models works.

Autoencoding Models as The Mind Readers

Imagine having a friend who can listen to your story, distill the essence, and then retell it in a nutshell, capturing every emotion and nuance. That’s pretty much what Autoencoding Models do with text. They’re phenomenal at grasping the context and generating outputs. They take your words, condense them to understand the vibe, and then unfold them to reveal the sentiment beneath. BERT, for example, uses bidirectional analysis, which means it looks both ways before crossing the context, ensuring it gets the full picture.

Autoregressive Models as The Fortune Tellers

Now, imagine another friend who’s great at telling stories. You give them a line, and they spin it into a tale, adding piece by piece. That’s the essence of Autoregressive Models. Technically speaking, they predict the next token, e.g., a word or character, based on what has already been generated, “regressing” on the previously generated sequence. They are ideal for crafting emails or continuing a story where you left off. The GPT series, with its knack for generating text that flows naturally, is a prime example of this category.

Sequence-to-Sequence Models as The Shape Shifters

Envision a translator who not only switches between languages but can also transform spoken words into written text and vice versa. That’s the realm of Sequence-to-Sequence Models. They’re pivotal in tasks that involve transformation or conversion, such as language translation, text summarization, and question answering. T5 and BART are champions here, known for their ability to adapt and accurately transform sequences in a multimodal world.

LLM Fine-Tuning Process

Fine-tuning LLMs is a process aimed at enhancing their performance for specific tasks. This tailored approach involves selecting the right prompts from the training dataset, generating high-quality completions, evaluating these against true labels for accuracy, and updating the model using advanced techniques.

Taking Prompt from Training Dataset

When selecting prompts from the training dataset for fine-tuning LLMs, it’s crucial to choose prompts that are varied and directly relevant to the specific tasks the model will perform. These prompts should be free from biases and ethically sound, ensuring that the model’s training process promotes fairness and neutrality.

Generating Completion from LLM

In the generation phase, prompts are fed into the LLM to elicit task-specific completions. These generated pieces should not only be coherent, making logical and contextual sense within the given prompt but also maintain a high standard of quality, reflecting the model’s understanding of the task.

Comparing LLM Completion Against Label

Evaluating the LLM’s completions involves comparing them against true labels to gauge accuracy and relevance. This step is critical for identifying biases or systematic errors in the LLM’s outputs. Employing both qualitative assessments, such as human judgment, and quantitative metrics, such as accuracy rates, provides a holistic view of the model’s performance and its alignment with objectives.

Updating LLM Using Cross-Entropy

The fine-tuning process uses cross-entropy loss to quantify how well the LLM’s predictions match the expected outcomes, providing a clear metric for its prediction errors. Adjusting the model based on this feedback allows for targeted improvements. Balancing the learning rate is key to ensuring the model neither overfits the training data nor fails to capture the underlying patterns, optimizing its performance on the task.

Fine-Tuning Methods

Fine-tuning methods like Instruction Prompt Tuning, Parameter-Efficient Prompt Tuning, and Prompt Tuning with Soft Prompts each offer unique approaches to enhancing pre-trained model performance. These methods range from optimizing for specific tasks to maintaining generalization across multiple tasks and ensuring parameter efficiency, tailoring models to precise requirements while balancing resource use and adaptability.

Instruction Prompt Tuning

Instruction Prompt Tuning adapts LLMs to specific tasks by providing detailed instructions in prompts, leveraging pre-trained knowledge efficiently without extensive retraining. It emphasizes the importance of clear, precise instructions for task-specific adaptations and requires iterative testing and refinement. This method offers a flexible, resource-efficient way to enhance model performance.

Single-Task Instruction Fine-Tuning

Single-task instruction fine-tuning focuses on optimizing a pre-trained model for a specific task using explicit instructions. While it enhances task-specific performance and efficiency, it risks catastrophic forgetting, where the model loses its ability to perform previously learned tasks due to the specialized focus of the fine-tuning process.

Multi-Task Instruction Fine-Tuning

To mitigate catastrophic forgetting, multi-task instruction fine-tuning optimizes a pre-trained model across several tasks simultaneously, using explicit instructions for each. It preserves the model’s generalization capabilities across tasks, though it may not reach the same level of task-specific performance optimization as the single-task method.

Parameter-Efficient Fine-Tuning

Parameter-efficient fine-tuning modifies a pre-trained model with minimal updates to its parameters, aiming for broad or specific task improvements. Unlike single-task or multi-task instruction fine-tuning, which may adjust many parameters per task, this approach alters fewer parameters, offering a compromise between model adaptability and efficiency without extensive retraining or risking catastrophic forgetting.

Prompt Tuning with Soft Prompts

Prompt Tuning with Soft Prompts involves embedding trainable vectors (soft prompts) into a pre-trained model to guide its performance on specific tasks, without modifying the model’s parameters. Unlike single-task or multi-task instruction fine-tuning, it’s more parameter-efficient, offering a flexible, less resource-intensive approach to task adaptation while maintaining broader model capabilities.

Model Evaluation Metrics

These metrics serve as the basis for evaluation:

Recall measures the proportion of actual positive cases that the model correctly identified out of all actual positive cases. It’s crucial for scenarios where missing a positive case has serious consequences, emphasizing the model’s ability to capture all relevant instances.

Precision calculates the fraction of correctly predicted positive cases among all cases predicted as positive. High precision is vital in situations where false positives are costly or undesirable, focusing on the accuracy of positive predictions.

F1 Score combines precision and recall into a single metric by calculating their harmonic mean. This metric is useful when balancing the trade-off between precision and recall is essential, providing a measure of a model’s accuracy in identifying positive cases while minimizing false positives and negatives.

ROUGE (Recall-Oriented Understudy for Gisting Evaluation) measures the quality of text summaries by comparing them to reference summaries. It evaluates the overlap of n-grams, word sequences, and word pairs between the generated text and reference, focusing on recall to ensure comprehensive coverage of the reference content.

BLEU (Bilingual Evaluation Understudy) is used in machine translation to assess the quality of translated text against one or more reference translations. It calculates the precision of n-grams in the translated text relative to the reference, with a penalty for overly short translations, emphasizing accuracy and fluency.

LLM Optimization for Inference

Optimizing LLMs for inference is crucial for enhancing their efficiency and applicability in real-world applications. Techniques like distillation, quantization, and pruning reduce resource consumption and improve speed, making LLMs more accessible and practical for deployment across various platforms.

Distillation involves training a smaller, more efficient model (the “student”) to mimic the behavior of a larger, pre-trained model (the “teacher”). This process retains the essential knowledge and performance of the larger model while enabling faster, more resource-efficient inference.

Quantization reduces the precision of the model’s parameters (e.g., from 32-bit floating point to 8-bit integers), decreasing memory usage and speeding up inference. Despite the reduced precision, quantization aims to maintain as much of the model’s original performance as possible.

Pruning selectively removes less important parameters or neurons from a model, reducing its complexity and size. This technique focuses on eliminating redundancy within the model’s architecture, leading to improvements in inference speed and efficiency without significantly compromising the model’s accuracy or performance.

Inference Parameters

Inference parameters like Max New Tokens, Sample Top K, Sample Top P, and Temperature are crucial for controlling and refining the output of LLMs during generation. They help balance creativity and coherence, manage output length, and tailor randomness, ensuring that generated text meets specific quality, relevance, and stylistic requirements for diverse applications.

Max New Tokens specifies the maximum number of tokens the model will generate in response to a prompt during inference. This parameter helps control the length of the generated output, ensuring responses are concise and within desired constraints, optimizing both relevance and computational efficiency.

Sample Top K limits the model’s choices to the top K most likely the next tokens at each step of generation. Focusing on a subset of highly probable tokens guides the model towards more coherent and contextually appropriate outputs, reducing the likelihood of irrelevant diversions.

Sample Top P (also known as nucleus sampling) selects the smallest set of tokens whose cumulative probability exceeds the threshold P, allowing for more dynamic and diverse generation than top-K sampling by adapting the token pool size based on distribution sharpness at each step.

Temperature adjusts the randomness of the model’s token selection process. A lower temperature makes the model’s choices more deterministic, favoring higher probability tokens, while a higher temperature increases diversity in the generated output, encouraging more variability and creativity in responses.

Augment The Models

Retrieval Augmented Generation (RAG) and Reasoning and Action (ReAct) are two prominent features that augment traditional language generation with external knowledge and reasoning capabilities, enhancing LLMs’ utility in complex tasks requiring up-to-date information, deep understanding, and logical reasoning.

RAG (Retrieval-Augmented Generation)

Retrieval Stage: RAG begins with an input query, which it uses to perform a search across a vast external corpus, such as Wikipedia, to retrieve relevant documents. This retrieval is powered by a dense vector search mechanism, where both the query and the documents are represented as vectors in a high-dimensional space, allowing for the computation of relevance based on their proximity.

Augmentation Stage: The retrieved documents are then concatenated with the original query to serve as an augmented input, providing a rich context that includes both the query and the relevant external knowledge.

Generation Stage: This augmented input is fed into LLMs, which synthesize the information to produce a coherent and contextually enriched output. The LLMs generate responses that not only align with the input query but also incorporate details and factual information from the retrieved documents, thereby enhancing the quality and informativeness of the output.

ReAct (Reasoning and Action)

Understanding Stage: ReAct starts by processing the input text to grasp the context, identify key entities, and understand the task or question posed. This process ensures a deep comprehension of the language and the specifics of the scenario.

Reasoning Engine: At its core, ReAct integrates a reasoning engine capable of logical deduction, causal reasoning, and evaluating possible actions. This engine works on the information parsed in the understanding stage, applying logical rules, assessing causal relationships between entities, and formulating potential actions or solutions based on the given context.

Action Planning and Generation Stage: The outcome of the reasoning process, which includes deduced facts, predicted outcomes, or planned actions, is then used to guide the text generation process. The LLMs synthesize this structured reasoning output with the original input to generate text that not only responds to the query but also reflects a logical and reasoned understanding of the situation, offering solutions, explanations, or actions that are coherent and contextually appropriate.

Align with Human Feedback

Reinforcement Learning from Human Feedback (RLHF) and Constitutional AI represent advanced frameworks aimed at aligning AI behavior with human values and ethics. RLHF fine-tunes AI responses based on direct human feedback, while Constitutional AI embeds fundamental rights and societal norms into AI’s decision-making, ensuring technology serves humanity responsibly and ethically.

RLHF (Reinforcement Learning from Human Feedback)

Incorporating RLHF within these structured objectives — maximizing helpfulness, minimizing harm, avoiding dangerous topics, and applying a KL divergence shift penalty — provides a comprehensive framework for developing AI models that are not only effective and user-friendly but also ethically responsible and socially aware, balancing the need for adaptability with the importance of maintaining a reliable and consistent knowledge foundation.

Maximize Helpfulness: RLHF aims to train models to generate responses that are accurate and helpful to users. By incorporating human feedback into the training process, the model learns to prioritize outputs that provide the most value, utility, and satisfaction to users according to human standards.

Minimize Harm: A critical aspect of RLHF is training models to minimize potential harm in their responses. This includes avoiding the generation of misleading, incorrect, or harmful content. Through feedback loops, models are taught to recognize and steer clear of responses that could cause misunderstanding, spread misinformation, or otherwise harm the user.

Avoid Dangerous Topics: RLHF incorporates mechanisms to identify and avoid topics or content areas that are considered dangerous, sensitive, or inappropriate. By analyzing human feedback, the model learns to navigate these topics carefully, either by generating safe, neutral responses or by declining to engage in such topics altogether.

KL Divergence Shift Penalty: To maintain a balance between following the training distribution and adapting to new patterns from human feedback, RLHF employs techniques like KL (Kullback-Leibler) Divergence Shift Penalty. This approach penalizes large deviations from the original model behavior, ensuring that while the model adapts to maximize helpfulness and minimize harm based on human feedback, it does not diverge too significantly from its foundational knowledge base.

Constitutional AI

Incorporating Constitutional AI principles within the structure of Helpful LLM, Red Teaming, and Response, Critique, and Revision represents a comprehensive approach to developing AI systems, which are not only technologically advanced but also aligned with human ethics and values.

Helpful LLM: Constitutional AI begins with the development of LLMs that are designed to be helpful, adhering to ethical guidelines and principles that reflect human rights and societal norms. This involves training models on diverse, ethically curated datasets and integrating mechanisms that allow them to understand and apply ethical considerations in their responses.

Red Teaming: In this phase, “Red Teams” composed of ethicists, psychologists, legal experts, and other stakeholders systematically challenge the LLMs by presenting them with complex, nuanced scenarios that test their ethical boundaries and decision-making capabilities. Red Teaming is crucial for exposing weaknesses and areas for improvement, ensuring the AI’s alignment with constitutional values under a wide range of conditions.

Response, Critique, and Revision: Following Red Teaming, the feedback and insights gained are used to refine and improve the LLMs. This involves addressing identified issues, enhancing the model’s understanding of ethical principles, and revising its decision-making processes to better align with constitutional guidelines. This iterative cycle of critique and revision is key to developing robust Constitutional AI that consistently respects and upholds human rights and societal norms.

To Sum Up

We’ve explored the key technical concepts and their application across the Generative AI development cycle, covering everything from fine-tuning and validation to enhancing and aligning with human feedback. This article is essentially a selection of key takeaways from a comprehensive library. Refer to the library below for more in-depth information on what’s been introduced.